A Complete Guide to Systematic Evidence Maps: Revolutionizing Chemical Risk Assessment for Researchers

Abstract

This article provides a comprehensive guide to Systematic Evidence Maps (SEMs), a transformative methodology for organizing and visualizing complex toxicological data in chemical risk assessment. Aimed at researchers, scientists, and drug development professionals, the content explores SEMs from foundational principles to advanced applications. It details how SEMs function as queryable databases to systematically characterize broad evidence bases, identify critical research gaps, and prioritize resources for subsequent systematic reviews or primary studies[citation:1][citation:5]. The article covers core methodological steps, including protocol development and data extraction, and presents real-world case studies from agencies like the US EPA[citation:6][citation:7]. It further addresses common implementation challenges, optimization strategies using knowledge graphs and automation[citation:2][citation:10], and validates SEMs by comparing them with other evidence synthesis tools. The conclusion synthesizes key takeaways and outlines future directions for integrating SEMs into biomedical and clinical research workflows to enhance evidence-based decision-making.

What Are Systematic Evidence Maps? Core Concepts and Evolution in Risk Science

In the field of chemical risk assessment, researchers and regulators are tasked with making critical decisions based on an expansive, complex, and often contradictory body of scientific evidence. Systematic Evidence Maps (SEMs) have emerged as a pivotal methodological tool to navigate this landscape. An SEM is defined as a form of evidence synthesis that offers a structured approach to categorizing and organizing scientific evidence to identify overarching trends and critical knowledge gaps [1]. Unlike a traditional systematic review, which aims to synthesize findings to answer a specific, narrow question, an SEM provides a broad, visual overview of an entire evidence base [2].

The application of SEMs is particularly valuable in environmental health and chemical risk management. Regulatory bodies, including the U.S. Environmental Protection Agency (EPA) and the Agency for Toxic Substances and Disease Registry (ATSDR), now routinely employ SEMs as problem-formulation tools and to support priority-setting in their assessment programs [3] [4]. For example, within the EPA's Integrated Risk Information System (IRIS), SEMs are used to systematically capture and screen literature on chemicals, creating an interactive inventory of research that informs subsequent, more targeted analyses [3]. By mapping the available evidence—including mammalian bioassays, epidemiological studies, and New Approach Methodologies (NAMs)—SEMs help decision-makers understand what is known, where robust evidence exists for systematic review, and where significant gaps warrant new primary research [2]. This "big picture" perspective is essential for efficient and transparent evidence-informed decision-making in chemical policy.

Core Methodology of Systematic Evidence Mapping

The methodological framework for conducting an SEM is rigorous and systematic, sharing several steps with traditional systematic reviews but differing in its objectives and final output. The process is designed to be comprehensive yet manageable for broad topic areas [1] [5]. The following workflow outlines the key stages.

Table: Systematic Evidence Map (SEM) Workflow

Defining Scope and Eligibility (PECO Framework)

The process begins with formulating a clear, often broad, research question. In chemical risk assessment, this is typically structured using the PECO framework (Population, Exposure, Comparator, Outcome) [3]. For an SEM, the PECO criteria are kept intentionally broad to capture a wide swath of potentially relevant evidence. The scope may also define supplemental content to track, such as in vitro studies, pharmacokinetic data, or evidence from New Approach Methods (NAMs) [3].

Systematic Search Strategy

A comprehensive and systematic search is conducted across multiple bibliographic databases and other sources. The challenge is balancing comprehensiveness with feasibility due to the broad scope [6]. Search strategies are designed to be sensitive, often requiring collaboration with information specialists. Key databases for environmental health topics typically include PubMed/MEDLINE, Embase, and Web of Science, with subject-specific databases added as needed [6] [7].

Screening and Data Coding

Identified records are screened against the eligibility criteria in multiple phases (title/abstract, then full-text), usually with two independent reviewers to minimize error [3]. Included studies then undergo data coding, where key metadata is extracted. This focuses on study characteristics (e.g., chemical, study type, model system, health outcome) rather than detailed quantitative results [1] [5]. This coded data forms the foundation for the evidence map.

Critical Appraisal (Risk of Bias Assessment)

Critical appraisal of individual studies is considered an optional step in an SEM [1] [3]. It is typically conducted when studies are categorized by the direction of effect or when the SEM is intended to directly inform a subsequent systematic review. When performed, it follows standard risk-of-bias assessment tools relevant to the study designs in question.

Data Visualization and Synthesis

The final and defining stage is the creation of interactive visualizations. Unlike a systematic review's narrative or meta-analytic synthesis, an SEM synthesizes evidence by categorizing and mapping it visually [2]. This is often achieved through heatmaps, interactive databases, or network diagrams that allow users to explore the evidence landscape, instantly see clusters of research, and identify empty cells representing evidence gaps [1].

Data Presentation: Evaluating Search Strategies for Evidence Mapping

A critical methodological challenge in SEMs is designing an efficient yet comprehensive search. Search Summary Tables (SSTs) provide transparent data on the performance of different information sources, guiding resource allocation in future projects [6] [7]. The following table summarizes data from a case study on peer support interventions, illustrating the relative yield of different databases for identifying systematic reviews (SRs) and randomized controlled trials (RCTs)—study designs also relevant to chemical risk assessment [6].

Table: Search Summary Table (SST) for an Evidence and Gap Map Case Study [6]

Information Source	Total References Retrieved	Included Systematic Reviews (SRs)	Included Randomized Trials (RCTs)	Key Function for Evidence Mapping
MEDLINE	1,123	27 (84%)	55 (90%)	Core biomedical database; essential for both SRs and primary studies.
PsycINFO	581	15 (47%)	42 (69%)	Key for subject-specific (e.g., neurotoxicology) behavioral outcomes.
CINAHL	877	23 (72%)	36 (59%)	Useful for public health and community exposure outcomes.
Embase	1,484	25 (78%)	Not Reported	Broad biomedical coverage, strong for pharmacological/toxicological data.
CENTRAL	Not Reported	Not Applicable	53 (87%)	Primary resource for identifying controlled clinical trials.
Forward Citation Searching	N/A	1 (3%)	14 (23%)	Highly effective for finding newer RCTs citing key older studies.

Experimental Protocols: Detailed SEM Methodology for Chemical Risk Assessment

The U.S. EPA has developed a standardized template for conducting SEMs within its chemical risk assessment programs [3]. The protocol below details the steps, incorporating standard systematic review practices adapted for mapping objectives.

Table: Detailed Experimental Protocol for an EPA Systematic Evidence Map [3]

Protocol Stage	Detailed Methodology	Tools & Standards	Purpose in Chemical Risk Assessment
1. Protocol Development	Define broad PECO; list supplemental evidence types (e.g., in vitro, NAMs, genotoxicity); pre-register plan.	PECO framework; ROSES checklist [5].	Ensures transparency, reduces bias, and sets manageable scope for broad chemical topics.
2. Search Strategy	Execute search in core databases (PubMed, TOXLINE, Embase); supplement with grey literature searches.	Boolean operators; controlled vocabularies (MeSH, Emtree).	Maximizes capture of all potentially relevant toxicological and epidemiological literature.
3. Screening	Dual-independent review at title/abstract and full-text levels using pre-defined forms; resolve conflicts by consensus.	Abstract screening software (e.g., Rayyan, SWIFT-Review).	Ensures reproducible and unbiased selection of studies against broad eligibility criteria.
4. Data Extraction & Coding	Extract metadata (study design, chemical, dose, model, outcome) into structured web-based forms; no synthesis of results.	Custom database platforms (e.g., Health Assessment Workspace Collaborative).	Creates a queryable database of study characteristics for visualization and gap analysis.
5. Study Evaluation (Optional)	Apply risk-of-bias tools (e.g., OHAT, NTP RoB) on a case-by-case basis if needed for prioritization.	Risk-of-bias assessment tools.	Provides a layer of quality assessment to inform confidence in evidence clusters.
6. Visualization & Reporting	Generate interactive heatmaps and evidence atlases; publish data in open-access formats.	Data visualization software (e.g., Tableau, R Shiny).	Enables stakeholders to interact with the evidence landscape and identify gaps intuitively.

SEMs vs. Systematic Reviews: A Functional Contrast

Understanding the distinction between SEMs and traditional systematic reviews (SRs) is crucial for selecting the appropriate evidence synthesis tool. The following diagram and table contrast their primary functions, processes, and outputs within the context of chemical risk assessment [1] [2].

Table: Functional Contrast Between Systematic Evidence Maps and Systematic Reviews [1] [3] [2]

Aspect	Systematic Evidence Map (SEM)	Traditional Systematic Review (SR)
Primary Question	Broad: "What is the extent and distribution of evidence on this chemical/outcome?"	Focused: "What is the effect of exposure X on health outcome Y?"
PECO Scope	Intentionally broad to capture all relevant evidence.	Highly specific to limit evidence to directly comparable studies.
Core Process	Systematic identification, categorization, and visual mapping of studies.	Systematic identification, critical appraisal, and statistical/narrative synthesis.
Data Extraction	Descriptive metadata (study design, population, exposure, outcome).	Detailed quantitative results and study characteristics for synthesis.
Critical Appraisal	Optional; not required for mapping purpose.	Mandatory; integral to interpreting findings and grading evidence.
Key Output	Interactive evidence atlas or heatmap showing evidence clusters and gaps.	Qualitative summary or meta-analysis with a strength-of-evidence conclusion.
Role in Decision-Making	Priority-setting: Identifies needs for future SRs or primary research.	Risk characterization: Directly informs hazard identification and dose-response.

Conducting a robust SEM requires a suite of methodological tools and resources. The following table details key "research reagent solutions" essential for the SEM process in chemical risk assessment.

Table: Essential Toolkit for Conducting Systematic Evidence Maps in Chemical Risk Assessment

Tool Category	Specific Item/Resource	Function in SEM Process	Example/Note
Protocol & Reporting Standards	ROSES (Reporting Standards for Systematic Evidence Syntheses) [5]	Provides a checklist for planning and reporting SEMs, ensuring methodological transparency.	Equivalent to PRISMA for systematic reviews but tailored for mapping.
Eligibility Framework	PECO (Population, Exposure, Comparator, Outcome) Statement [3]	Structures the broad research question and defines the boundaries for study inclusion.	In chemical risk, P: human/animal; E: specific chemical; C: unexposed/low dose; O: health outcome.
Search Resources	Core Biomedical Databases (PubMed/MEDLINE, Embase, Web of Science) [6] [7]	Primary sources for identifying published toxicological and epidemiological literature.	MEDLINE and Embase are considered essential for comprehensive retrieval [6].
Search Resources	Toxicology-Specific Databases (TOXLINE, ECOTOX)	Capture specialized literature on chemical effects not fully indexed in core biomedical databases.	Critical for environmental risk assessments.
Screening & Automation Tools	Machine Learning-Aided Screening Software (e.g., SWIFT-Review, ASReview)	Prioritizes references during screening, increasing efficiency for large result sets [3].	Learns from reviewer decisions to rank likely relevant records higher.
Data Management	Systematic Review Management Platforms (e.g., HAWC, DistillerSR)	Manages the flow of references, facilitates dual-independent screening, and stores extracted data [3].	EPA's Health Assessment Workspace Collaborative (HAWC) is specifically designed for risk assessment.
Visualization Software	Interactive Dashboard Tools (e.g., Tableau, R Shiny, Python Dash)	Transforms coded metadata into interactive heatmaps and evidence gap maps for exploration [1].	Allows end-users to filter and explore the mapped evidence by chemical, outcome, or study type.

The Problem: Volume, Velocity, and Variability

Modern toxicology is experiencing a fundamental crisis of information. The evidence base for assessing chemical risks has expanded exponentially due to factors including more sensitive analytical techniques, increased regulatory data requirements, and the reform of regulatory reliance on traditional in vivo toxicity testing [8]. This has led to a scenario characterized by overwhelming volume, high velocity of new data generation, and significant variability in data types and quality. Consequently, locating, organizing, and evaluating all relevant data for informed decision-making has become a formidable challenge [8].

The regulatory landscape is simultaneously becoming more complex. Global frameworks are evolving toward stricter sustainability mandates, broader restrictions on substances like PFAS, and the digitalization of compliance reporting [9]. For instance, the European Union's Chemicals Strategy for Sustainability (CSS) and initiatives like the Safe-and-Sustainable-by-Design (SSbD) framework demand more comprehensive, predictive, and mechanistic data [9] [10]. This creates a critical gap: the need for robust, evidence-based decisions is greater than ever, but the traditional tools for evidence synthesis are ill-equipped to handle the modern data deluge.

This data overload directly impedes core toxicological and regulatory workflows, including:

• Hazard Identification & Characterization: Difficulty in aggregating fragmented evidence from high-throughput in vitro assays, omics technologies, and traditional studies to form a coherent hazard profile.
• Risk Assessment of Mixtures: Assessing cumulative exposure and "cocktail effects" is nearly intractable with conventional methods, despite evidence that simultaneous exposure to low doses of different pesticides can result in additive or synergistic effects [11].
• Application of New Approach Methodologies (NAMs): Integrating data from diverse NAMs—such as in silico modeling, high-throughput screening, and toxicogenomics—into a unified assessment framework [11] [10].
• Regulatory Prioritization & Scoping: Identifying critical data gaps and prioritizing chemicals for thorough risk evaluation amidst vast datasets.

Table 1: Key Data Challenges in Modern Chemical Risk Assessment

Challenge Dimension	Specific Manifestation	Impact on Risk Assessment
Volume	Exponential growth in published studies, regulatory dossiers (e.g., IUCLID), and high-throughput screening data [8].	Key evidence is overlooked; systematic review becomes prohibitively resource-intensive.
Variability (Heterogeneity)	Data from diverse sources (academic, regulatory, industry), study types (in vivo, in vitro, in silico), and reporting formats [8].	Difficult to compare, combine, or synthesize findings across the evidence base.
Velocity	Rapid generation of new data from automated platforms and evolving scientific techniques [8].	Evidence assessments are outdated by the time they are completed.
Veracity (Uncertainty)	Variable study quality, reporting completeness, and relevance of model systems to human health [11].	Undermines confidence in conclusions and complicates weight-of-evidence analyses.
Regulatory Complexity	Evolving requirements under EU CSS, TSCA, GHS revisions, and mixture assessment mandates [9] [11].	Increases the breadth of data required for compliance and safe-by-design innovation.

Systematic Evidence Maps: A Foundational Solution

Systematic Evidence Mapping (SEM) emerges as a foundational methodology to address these challenges. An SEM is defined as a queryable database of systematically gathered and structured evidence, designed to organize and characterize a broad evidence base for exploration by diverse end-users [8]. Unlike a systematic review, which aims to answer a specific, narrow question with synthesis, an SEM aims to provide a map of the available evidence landscape. It enables users to identify clusters of research, glaring gaps, and trends without initially committing to a single synthesis question [8].

The core value proposition of SEM in toxicology is its role in facilitating evidence-based approaches while managing scale. It provides a transparent, auditable, and reusable resource that:

• Collates fragmented data into a single access point.
• Structures unstructured data (e.g., extracting key parameters from PDFs into defined fields).
• Codes data using controlled vocabularies and ontologies, enabling meaningful comparison across heterogeneous studies [8].

This is particularly vital for toxicology, where framing a single, narrow systematic review question is often difficult or uninformative for broad policy or prioritization needs [8]. An SEM serves as the critical first step in a tiered evidence-synthesis strategy, enabling efficient prioritization of resources for full systematic review where it is most needed.

From Flat Tables to Knowledge Graphs: An Architectural Evolution

Traditional SEMs, often built on relational databases with rigid, flat table structures, are insufficient for modern toxicology's interconnected data. This "schema-on-write" approach struggles with the highly connected and heterogeneous nature of toxicological data, where relationships (e.g., between a chemical, a molecular target, an adverse outcome pathway, and a disease) are as important as the entities themselves [8].

The next-generation architecture for SEMs is the knowledge graph. A knowledge graph is a flexible, schemaless data model that stores information as a network of nodes (entities/concepts) and edges (relationships). This "schema-on-read" approach is inherently suited for toxicology because it can easily accommodate [8]:

• Diverse and evolving data types without pre-defined table structures.
• Complex, multi-step relationships (e.g., part of an Adverse Outcome Pathway).
• Integration with formal ontologies (shared, logically related controlled vocabularies), which provide semantic meaning and enable sophisticated computational reasoning [8].

Table 2: Relational Database vs. Knowledge Graph for Toxicological SEMs

Feature	Traditional Relational (Schema-on-Write)	Knowledge Graph (Schema-on-Read)
Data Structure	Rigid, predefined tables and columns.	Flexible, graph-based (nodes/edges).
Schema Definition	Required before data ingestion.	Applied during data querying and interpretation.
Relationship Handling	Handled via foreign keys between tables; complex relationships are cumbersome.	Relationships are first-class citizens, easily representing multi-step pathways.
Adaptability	Poor; adding new data types requires schema modification.	High; new node and relationship types can be added dynamically.
Query Focus	"What are the properties of X?"	"How is X connected to Y through Z?"
Suitability for Toxicology	Low; struggles with interconnected, heterogeneous data [8].	High; ideal for AOPs, mechanistic networks, and integrated data [8].

The following diagram illustrates the architectural shift and workflow for building a toxicological knowledge graph.

Diagram 1: Systematic Evidence Mapping Workflow & Architecture Evolution (width=760px)

Implementing a Toxicological SEM: A Technical Protocol

The development of a fit-for-purpose SEM for toxicology requires a meticulous, protocol-driven approach. The following workflow, derived from established methodology [8], outlines the key stages.

Protocol Development & Stakeholder Engagement

• Define Map Scope & Objectives: Clearly articulate the chemical, toxicological, or regulatory domain (e.g., "endocrine disruption potential of pesticides"). Engage regulatory scientists, risk assessors, and researchers to ensure relevance.
• Develop a Detailed A Priori Protocol: Publish a protocol specifying the search strategy, data sources, inclusion/exclusion criteria, data extraction fields, and coding strategy. This is critical for transparency and reproducibility [8].

Evidence Search, Screening & Extraction

• Systematic Searching: Execute searches across multiple bibliographic (PubMed, Scopus, Embase) and regulatory (ECHA, EPA) databases. Search strings must balance sensitivity and specificity.
• Screening: Implement a two-stage (title/abstract, then full-text) screening process using tools like Rayyan or Covidence. At least two independent reviewers mitigate bias.
• Data Extraction: Extract structured data into a predefined template. Critical fields for toxicology include: chemical identifier (CAS, name), study type (in vivo/in vitro/in silico), test system, endpoint measured, dose/response data, and reported outcome.

Data Coding & Ontology Alignment

This is the most critical step for enabling interoperability and sophisticated querying.

• Code Development: Create a coding "book" of controlled terms for key variables (e.g., species, sex, target organ).
• Ontology Integration: Map codes to established biomedical ontologies. For example:
  • Chemicals: ChEBI (Chemical Entities of Biological Interest)
  • Assays & Endpoints: OBI (Ontology for Biomedical Investigations), BAO (BioAssay Ontology)
  • Diseases & Phenotypes: Mondo (Monarch Disease Ontology), MP (Mammalian Phenotype Ontology)
  • Adverse Outcome Pathways: AOP-Wiki concepts
• This semantic alignment allows the graph to "understand" that "hepatocellular carcinoma" (from Mondo) and "liver tumor" (from a study abstract) are related concepts.

Graph Construction & Quality Assurance

• Node & Edge Creation: Using a graph database platform (e.g., Neo4j, Amazon Neptune, or a triplestore like Stardog), transform the coded data into a graph. Each study, chemical, and outcome becomes a node. Relationships like Chemical-[CAUSES]->Effect or Study-[USES_ASSAY]->Assay become edges.
• Data Integrity Checks: Implement rigorous quality control to ensure accurate extraction, coding, and graph population. Review a random sample of entries.

Table 3: Experimental Protocol for a High-Throughput Screening (HTS) Data Integration Pilot

Protocol Stage	Action	Tools & Standards	Output/Deliverable
1. Scope Definition	Focus on estrogen receptor (ER) activity HTS data from Tox21/ToxCast.	–	Published study protocol.
2. Data Acquisition	Download curated data from EPA's CompTox Chemistry Dashboard.	CSV/JSON formats, DTXSIDs (chemical identifiers).	Raw HTS response data.
3. Data Extraction & Curation	Extract chemical ID, assay name (e.g., ATG_ERa_TRANS), AC50 values, hit-call.	Python/R scripts, OECD QSAR Toolbox.	Cleaned, structured dataset.
4. Ontological Coding	Map assay ATG_ERa_TRANS to BAO: BAO_0002179 (nuclear receptor transcription assay). Map "active" hit-call to OBI: OBI_0000312 (positive result).	Ontology lookup services (OLS), manual curation.	Annotated dataset with ontology URIs.
5. Graph Ingestion	Ingest data into Neo4j: Create Chemical nodes, Assay nodes, and HAS_ACTIVITY relationships with properties (AC50, hit-call).	Neo4j Cypher queries, Python driver.	Populated knowledge graph subset.
6. Query & Validation	Execute query: "Find all chemicals active in ERα assays and link to known ERα agonists from peer-reviewed literature."	Cypher query language.	Validated subgraph connecting HTS predictions to legacy knowledge.

The Scientist's Toolkit: Research Reagent Solutions for SEM Implementation

Building and utilizing a modern SEM requires a suite of technical and informatics "reagents."

Table 4: Essential Research Reagent Solutions for Toxicological SEMs

Tool Category	Specific Item/Technology	Function & Role in SEM
Data Storage & Management	Graph Database (Neo4j, Amazon Neptune, Stardog)	Core infrastructure for storing the knowledge graph, enabling efficient traversal of complex relationships [8].
Ontology Resources	Bioportal / OLS (Ontology Lookup Service), ChEBI, BAO, AOP-Wiki	Provides standardized, machine-readable vocabularies for coding toxicological entities and processes, ensuring semantic interoperability [8].
Data Extraction & Curation	Text Mining & NLP Tools (e.g., custom Python/R scripts, CLAMP)	Automates the extraction of key entities (chemicals, endpoints) from unstructured text in study abstracts and reports.
Chemical Registry	EPA CompTox Chemistry Dashboard, PubChem	Provides authoritative chemical identifiers (DTXSID, CID), structures, and links to associated property and toxicity data, crucial for node disambiguation.
Evidence Synthesis Platforms	Systematic Review Management Software (Rayyan, Covidence, DistillerSR)	Facilitates the collaborative screening and data extraction phases of the SEM workflow, managing reviewer conflict resolution.
Query & Visualization	Graph Query Languages (Cypher, SPARQL), Visualization Libraries (Cytoscape, Gephi)	Allows researchers to interrogate the graph (e.g., "find paths between chemical X and disease Y") and visualize complex networks.
Computational Toxicology Integration	OECD QSAR Toolbox, EPA OPERA, KNIME/Analytics Platform	Enriches chemical nodes with predicted properties and read-across hypotheses, bridging the SEM with New Approach Methodologies (NAMs) [10].

Applications and Impact on Chemical Risk Assessment

When operationalized, a graph-based SEM transforms key toxicological and regulatory workflows. Its primary power lies in enabling complex, relationship-focused queries that are impossible with traditional databases.

Application 1: Accelerated Problem Formulation & Scoping

• Scenario: A regulator needs to prioritize chemicals for evaluation of potential developmental neurotoxicity (DNT).
• SEM Action: Query the graph for chemicals with at least two weak associative links to DNT outcomes (e.g., active in a DNT-relevant in vitro assay AND structurally similar to a known DNT toxicant).
• Impact: Rapid identification of candidate chemicals for further evaluation, making priority-setting evidence-based and transparent.

Application 2: Mechanistic Hypothesis Generation for Mixture Risk

• Scenario: Assessing the potential for synergistic effects of a chemical mixture found in drinking water.
• SEM Action: For each chemical, retrieve its known molecular initiating events (MIEs) and key events (KEs) from linked AOP knowledge. Query for shared or interconnected KEs within the mixture.
• Impact: Identifies plausible mechanistic bases for additive or synergistic interactions, guiding targeted testing. This addresses the critical challenge of "cocktail effects" [11].

Application 3: Bridging New Approach Methodologies (NAMs) with Traditional Evidence

• Scenario: Validating a novel in vitro assay intended to predict liver steatosis.
• SEM Action: Assemble a "ground truth" subgraph of chemicals known to cause steatosis from legacy in vivo studies. Connect these to their in vitro bioactivity profiles from HTS.
• Impact: Enables the systematic evaluation of the assay's predictive capacity across a broad chemical space, supporting the regulatory acceptance of NAMs [10].

The internal structure of such a knowledge graph, focusing on the integration of diverse evidence streams, is shown below.

Diagram 2: Knowledge Graph Structure Integrating Diverse Evidence Streams (width=760px)

The driving need in modern toxicology is not merely for more data, but for intelligent data architecture. The complexity and volume of information have outstripped the capacity of traditional, linear review processes. Systematic Evidence Mapping, particularly when implemented using flexible, graph-based architectures, provides a transformative solution. It shifts the paradigm from static literature reviews to dynamic, queryable evidence ecosystems.

By moving from rigid tables to interconnected knowledge graphs, toxicologists and risk assessors can navigate the evidence landscape with unprecedented efficiency. This enables them to ask and answer complex, systems-level questions about chemical hazards, mixture risks, and mechanistic pathways. As regulatory frameworks evolve toward greater demands for safety, sustainability, and transparency [9] [10], investing in the development of these robust evidence-mapping infrastructures is not just an academic exercise—it is a fundamental prerequisite for achieving evidence-based chemical risk assessment in the 21st century.

The field of chemical risk assessment is undergoing a fundamental shift in how it synthesizes and utilizes scientific evidence. The traditional paradigm, anchored by the systematic review (SR), is being supplemented and transformed by the emergence of systematic evidence maps (SEMs). This evolution responds directly to the pressing needs of modern regulatory science: to manage vast, heterogeneous evidence bases efficiently, support priority-setting, and inform decisions within realistic timeframes [2] [3]. This guide details the historical context, methodological core, and practical application of this evolution, framing it within the critical domain of chemical risk assessment research.

The Catalysts for Evolution: Limitations of Systematic Review in Regulatory Science

Systematic reviews established the gold standard for evidence-based decision-making by introducing rigorous, protocol-driven methods to minimize bias and maximize transparency [2]. In chemical risk assessment, their adoption promised to address challenges like selective use of data ("cherry-picking") and inconsistent application of scientific judgment [2]. The core steps and advantages of SR are well-defined, as summarized in Table 1.

Table 1: Core Steps and Advantages of Systematic Review (SR) in Chemical Risk Assessment [2]

Systematic Review Step	Primary Advantage in Risk Assessment
Pre-published protocol	Reduces expectation bias; allows for external peer review of methods.
Clear PECO statement	Provides a structured, focused framework for the research question.
Comprehensive search	Reduces risk of partial retrieval of the relevant evidence base.
Screening against eligibility criteria	Reduces selection bias in deciding which evidence to include.
Data extraction & critical appraisal	Ensures consistent, valid interpretation of individual study findings.
Evidence synthesis & confidence rating	Increases power to identify trends; transparently communicates overall reliability of the body of evidence.
Drawing conclusions	Provides direct, synthesized answers to focused health risk questions.

However, the practical application of SR in regulatory workflows revealed significant limitations [2]:

• Resource Intensity: Full SRs are time-consuming and costly, ill-suited for the rapid pace of regulatory decision-making and the volume of chemicals requiring assessment.
• Narrow Scope: The focused PECO (Population, Exposure, Comparator, Outcome) format answers specific questions but is poorly suited for scoping broad evidence landscapes, identifying research trends, or prioritizing which chemicals or health endpoints merit a full SR.
• Static Output: SRs provide a snapshot in time and are difficult to update continuously amid a rapidly growing scientific literature.

These limitations created a methodological gap, particularly for agencies like the U.S. EPA, which must triage and evaluate thousands of chemicals under statutes like TSCA [2] [12]. The need was for a tool that retained the systematicity and transparency of SR but offered a broader, more flexible, and resource-efficient overview of the evidence. This need catalyzed the evolution toward systematic evidence mapping.

Defining the Paradigm Shift: Systematic Evidence Maps as a Strategic Tool

A Systematic Evidence Map (SEM) is defined as a systematically gathered database that characterizes broad features of an evidence base [2]. Unlike an SR, which synthesizes findings to answer a specific question, an SEM organizes and catalogs evidence to visualize the extent, distribution, and characteristics of available research.

The evolution from SR to SEM represents a shift from a definitive answer-generating engine to a strategic intelligence and planning tool. This shift is characterized by key differences in objectives, processes, and outputs, as detailed in Table 2.

Table 2: Comparative Analysis: Systematic Review vs. Systematic Evidence Map [2] [1] [3]

Feature	Systematic Review (SR)	Systematic Evidence Map (SEM)
Primary Objective	To synthesize evidence to answer a specific, narrow question (e.g., "Does chemical X cause outcome Y?").	To survey, categorize, and visualize the broad landscape of evidence on a topic (e.g., "What is known about all health effects of chemical class Z?").
Research Question	Tightly focused, defined by a precise PECO statement.	Broadly scoped, often using a modified PECO to capture a wide range of evidence.
Eligibility Criteria	Strict, designed to include only studies directly relevant to the synthesis.	More inclusive, often capturing studies for characterization even if not suitable for meta-analysis.
Critical Appraisal	Mandatory; risk of bias assessment is central to interpreting synthesized results.	Optional or streamlined; often conducted later if the map informs a subsequent SR [1].
Core Output	A quantitative or qualitative synthesis (e.g., meta-analysis) with a graded confidence assessment.	A searchable database and interactive visualizations (e.g., heatmaps, network diagrams) showing evidence clusters and gaps [1].
Key Utility	Provides a direct, evidence-based answer for risk management decisions.	Informs research prioritization, identifies needs for primary research or targeted SRs, and supports problem formulation in risk assessment [2] [3].

In chemical risk assessment, SEMs are now routinely used as problem formulation tools. They help assessors understand what types of studies exist (e.g., in vivo, in vitro, epidemiological), for which health endpoints, and for which exposure scenarios [3]. This allows for "fit-for-purpose" assessments where the depth of analysis can be tailored to the likelihood of risk, a principle reflected in recent regulatory proposals [12]. For example, the U.S. EPA's IRIS and PPRTV programs use SEMs as a critical first step in assessment development [3].

Methodological Protocols for Systematic Evidence Mapping

The strength of an SEM lies in its rigorous, protocol-driven methodology, which inherits the systematic search and transparency standards of SR while adapting other steps for mapping purposes. The following workflow, derived from established guidance and protocols, details the core steps [1] [3] [13].

Systematic Evidence Mapping (SEM) Standard Workflow [1] [13]

Step 1: Define Scope and Develop Protocol The process begins with a broad, strategic question. A pre-published protocol defines the objectives and methods. Key stakeholders, including research communities or affected interest groups, are often engaged to ensure relevance and utility [13]. The PECO criteria are kept broad to capture a wide swath of evidence. For example, a map on environmental chemicals and autism (aWARE project) includes human, non-human primate, and rodent studies across all exposure categories and ASD-related outcomes [13].

Step 2: Conduct Systematic Search A comprehensive, reproducible search strategy is developed for multiple bibliographic databases (e.g., PubMed, Web of Science, Scopus) without restrictive date or language filters [13]. This ensures the map captures the full breadth of relevant literature.

Step 3: Screen Studies Records are screened in two phases (title/abstract, then full text) against the eligibility criteria, typically using specialized systematic review software (e.g., DistillerSR) and following best practices to minimize bias [13].

Step 4: Extract and Code Data This is the core mapping activity. Data from included studies is extracted into structured, web-based forms. Coding focuses on characteristics needed for categorization and visualization, such as:

• Chemical/exposure class
• Study type (e.g., cohort, case-control, animal bioassay, in vitro)
• Health system or outcome assessed
• Model organism (if applicable)
• Study population demographics [3] [13] The U.S. EPA template also codes for supplemental content like New Approach Methodologies (NAMs) data and pharmacokinetic studies [3].

Step 5: Critical Appraisal (Optional) Formal risk-of-bias assessment is not always required for mapping. It may be conducted later if the map is used to select studies for a subsequent SR, or performed in a streamlined way to categorize studies by general reliability [1].

Step 6: Develop Interactive Visualization and Database The coded data is uploaded to interactive visualization platforms (e.g., Tableau, bespoke web applications) to create the SEM. Outputs are designed to be queryable, allowing users to filter and explore the evidence base dynamically [3] [13]. The aWARE project, for instance, is building a Web-based tool for this purpose [13].

Step 7: Narrative Summary and Report The final step involves interpreting the visualization to produce a narrative summary. This report identifies key evidence clusters (well-studied areas), critical evidence gaps (unstudied or understudied areas), and trends in the literature. This analysis directly informs recommendations for future primary research or targeted systematic reviews [2] [1].

Advanced Integration: SEMs with Adverse Outcome Pathways (AOPs) and New Approach Methodologies (NAMs)

The most advanced application of SEMs in chemical risk assessment is their integration with mechanistic toxicology frameworks. This represents the forward edge of the evolution from evidence synthesis to evidence-based predictive toxicology.

Systematic maps can be powerfully coupled with Adverse Outcome Pathway (AOP) development [14]. An AOP is a conceptual framework linking a molecular initiating event (MIE) through key biological events to an adverse outcome relevant to risk assessment. SEMs can be used to systematically survey and catalogue the literature supporting each key event relationship within a proposed AOP.

Integration of SEMs with AOPs and NAMs for Risk Assessment [3] [14]

This integration creates a data-driven, transparent bridge between mechanistic data and apical outcomes. For instance, an SEM on a liver toxicant would catalog not just traditional animal studies showing liver necrosis, but also in vitro studies showing receptor activation, omics studies revealing pathway perturbation, and epidemiological data. When mapped onto an AOP for liver fibrosis, this reveals which key event relationships are strongly supported and which are weak or missing [14].

Furthermore, SEMs explicitly track the availability of New Approach Methodologies (NAMs)—including high-throughput screening, transcriptomics, and in silico models—as supplemental content [3]. This practice directly supports the regulatory transition toward more efficient, human-relevant toxicity testing strategies by clarifying where traditional data can be supplemented or replaced with mechanistic NAM data.

Conducting a high-quality SEM requires a suite of specialized tools and reagents. The following table details key components of the modern evidence mapper's toolkit.

Table 3: Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Specific Item / Software	Function in SEM Process
Protocol & Project Management	Pre-registration platforms (e.g., PROSPERO, Open Science Framework)	Ensures transparency, reduces bias, and allows for peer review of the SEM plan before work begins.
Search & Screening Automation	Bibliographic databases (PubMed, Scopus, Web of Science); AI-assisted screening tools (e.g., SWIFT-Review, RobotAnalyst)	Enables comprehensive literature retrieval and uses machine learning to prioritize records during title/abstract screening, increasing efficiency [3].
Dedicated Review Software	DistillerSR, Rayyan, EPPI-Reviewer	Manages the entire review process—from reference importing, de-duplication, and multi-phase screening to data extraction and reporting—in a single, audit-ready platform [13].
Data Extraction & Coding	Custom web-based extraction forms (e.g., DEXTR); Standardized taxonomy ontologies	Provides structured, consistent fields for data capture (e.g., chemical, study design, outcome). Ontologies ensure standardized terminology across mappers [13].
Visualization & Database Creation	Business Intelligence software (Tableau, Power BI); Interactive web frameworks (R Shiny, Python Dash)	Transforms coded data into interactive heatmaps, bubble plots, and network diagrams. Allows creation of public-facing, queryable evidence databases [1] [13].
Integration with Toxicity Frameworks	AOP-Wiki (aopwiki.org); CompTox Chemicals Dashboard	Provides formal AOP structures to map evidence against and gives access to curated chemical data to inform coding and analysis [14].

Quantitative Applications and Impact in Chemical Risk Assessment

The value of SEMs is demonstrated through concrete applications and measurable outcomes in regulatory and research settings. The following table summarizes key quantitative insights and applications derived from the methodology.

Table 4: Quantitative Applications and Impact of Systematic Evidence Maps

Application Area	Quantitative Insight / Impact	Example from Evidence
Research Prioritization	Identifies the proportion of studies focused on specific health endpoints vs. others, revealing relative investment and attention.	An SEM on a chemical class may show 60% of studies investigate cancer, 20% investigate reproductive effects, and only 5% investigate neurotoxicity, clearly highlighting the latter as a priority gap [2].
Efficiency in Systematic Review	Reduces the resource burden of subsequent SRs by pre-identifying and categorizing the relevant evidence base.	The U.S. EPA uses SEMs as a mandated first step in IRIS assessments, allowing teams to quickly scope the available literature before committing to a full, resource-intensive SR [3].
Trend Analysis	Tracks the growth of specific research areas (e.g., NAMs) over time through publication year analysis.	A map can quantify the annual increase in publications using high-throughput transcriptomics for endocrine disruptors, demonstrating the field's evolution [3].
Regulatory "Fit-for-Purpose" Analysis	Informs the scope and depth of risk evaluations by categorizing evidence volume and type.	Supports proposed regulatory changes where analysis can be tailored: detailed assessment for high-exposure/high-hazard uses, and streamlined review for low-exposure, data-poor uses [12].
Stakeholder Communication	Provides visual, accessible summaries of complex evidence landscapes for policymakers and the public.	Projects like aWARE develop interactive web tools to communicate the state of science on autism and environment to the research community and interested public [13].

The evolution from systematic review to systematic evidence mapping represents more than a methodological tweak; it is a strategic adaptation of evidence-based science to the realities of modern chemical regulation. SEMs address the core challenges of volume, velocity, and variety in scientific data by providing a rigorous, transparent system for evidence triage and landscape visualization.

The future of this evolution points toward greater automation, integration, and dynamic updating. Machine learning and natural language processing will further streamline screening and data extraction [1]. The integration of SEMs with AOPs and NAMs will mature, creating living, evidence-linked knowledge frameworks that continuously incorporate new data [14]. Finally, the concept of "living" evidence maps that are periodically updated will transform SEMs from static reports into continuous evidence surveillance systems.

For researchers and assessors in chemical risk assessment, mastering SEM methodology is no longer optional but essential. It provides the critical link between the overwhelming deluge of primary research and the actionable, synthesized evidence required to protect public health efficiently and credibly.

Systematic Evidence Maps (SEMs) represent a transformative methodological advancement within chemical risk assessment, designed to characterize broad evidence landscapes and identify critical research gaps with greater efficiency than traditional systematic reviews [2] [15]. Functioning as queryable databases of systematically gathered research, SEMs provide a comprehensive overview of available evidence, supporting priority-setting for risk management and guiding targeted primary research or deeper systematic reviews [8] [3]. This technical guide details the core objectives, methodologies, and applications of SEMs, framing them within the evolving paradigm of evidence-based chemical regulation. It outlines standardized protocols for SEM construction, including problem formulation, evidence retrieval, and data extraction, while introducing advanced analytical techniques such as non-targeted analysis and knowledge graph integration for managing complex, heterogeneous data [16] [8]. The integration of SEMs into regulatory workflows, as exemplified by frameworks from the US EPA IRIS program and the European PARC initiative, demonstrates their critical role in enhancing the transparency, efficiency, and scientific robustness of global chemical safety decisions [3] [17].

The field of chemical risk assessment is characterized by an exponentially growing and heterogeneous evidence base, encompassing toxicological, epidemiological, exposure, and mechanistic data. Traditional narrative reviews and even rigorous systematic reviews (SRs) face significant challenges in this context. While SRs provide a gold standard for synthesizing evidence to answer a specific, focused question (e.g., "Does chemical X cause outcome Y in population Z?"), they are resource-intensive and their narrow scope can be misaligned with the broad evidence needs of regulators and risk managers tasked with evaluating thousands of substances [2] [18].

Systematic Evidence Maps (SEMs) have emerged as a novel tool to bridge this gap. An SEM is defined as a queryable database of systematically gathered research that characterizes the broad features of an evidence base [2] [15]. The core objectives of an SEM are twofold:

• Characterizing the Evidence Landscape: To systematically catalog and describe the available scientific literature on a given chemical, class of chemicals, or health outcome, including the volume, distribution, and key characteristics of studies (e.g., study designs, model systems, exposure levels, endpoints measured).
• Identifying Critical Gaps and Clusters: To visually and analytically reveal where sufficient evidence exists to support a definitive SR (evidence clusters) and where significant knowledge gaps or uncertainties remain, thereby guiding future research and resource allocation [8] [3].

Unlike an SR, an SEM does not aim to synthesize data to estimate a pooled effect size or provide a definitive hazard conclusion. Instead, it serves as a critical precursor and prioritization tool, making the evidence landscape navigable and informing where the application of more intensive SR methods would be most valuable [2]. This approach aligns with the needs of modern regulatory initiatives like the EU's REACH and the US TSCA, which require efficient, transparent, and evidence-based management of large chemical inventories [15] [17].

Methodological Framework for SEM Development

Core Workflow and Problem Formulation

The development of an SEM follows a rigorous, protocol-driven workflow to ensure transparency, reproducibility, and minimization of bias. The process begins with problem formulation, where a broad but structured review question is established. This is often framed using a modified PECO (Population, Exposure, Comparator, Outcome) statement, which is kept broader than in an SR to capture a wide swath of relevant evidence [3]. For example, an SEM on a class of pesticides might define its PECO as: Population (all mammalian laboratory animals and human epidemiological cohorts), Exposure (any study investigating exposure to chemicals within the defined class), Comparator (unexposed or differently exposed controls), and Outcome (any health or biological endpoint) [3].

The subsequent workflow involves searching multiple bibliographic databases with a comprehensive search strategy, systematic screening of titles/abstracts and full texts against pre-defined eligibility criteria, and finally, data extraction and coding of included studies into a structured database [2].

Diagram 1: Systematic Evidence Map (SEM) Development Workflow

Data Extraction and Structuring: From Flat Tables to Knowledge Graphs

Traditionally, extracted data from systematic maps have been stored in flat, tabular formats (e.g., spreadsheets). However, the complex, interconnected nature of chemical risk assessment data—linking chemicals, molecular targets, toxicological outcomes, study models, and endpoints—makes this approach limiting [8].

The cutting-edge evolution in SEM methodology involves structuring data as a knowledge graph. A knowledge graph is a flexible, schemaless network of entities (nodes) and their relationships (edges) [8]. This model is inherently suited for environmental health data, allowing for intuitive representation of complex relationships (e.g., "Chemical A activates Receptor B, which leadsto Outcome C, as reportedin Study D") [8]. Knowledge graphs facilitate sophisticated querying and trend analysis that are cumbersome with flat tables, enabling a more dynamic and insightful characterization of the evidence landscape. This graph-based approach supports long-term goals of interoperability and reusability of evidence across different assessment bodies [8].

Diagram 2: Knowledge Graph Schema for Interconnected Evidence

Experimental Protocols for Evidence Generation and Analysis

Protocol for Evidence Retrieval and Screening (EPA IRIS/PPRTV Template)

The US EPA's Integrated Risk Information System (IRIS) program has developed a standardized template for SEMs that emphasizes rapid, "fit-for-purpose" production [3]. A key component is the use of machine learning-assisted screening.

• Step 1 – Broad Search & De-duplication: Execute search strings across multiple databases (e.g., PubMed, Scopus, Web of Science). Remove duplicates using algorithmic tools.
• Step 2 – Machine Learning Prioritization: Import titles/abstracts into specialized software (e.g., SWIFT-Review, DistillerSR). A small subset (~500) is manually screened by two reviewers to generate a training set. The machine learning model then scores and ranks the remaining records, placing the most likely relevant studies at the top of the workflow.
• Step 3 – Dual-Screen Review: Reviewers screen the prioritized list. This "active learning" approach significantly accelerates the identification of PECO-relevant studies (e.g., mammalian bioassays, epidemiology) while ensuring comprehensive coverage [3].
• Step 4 – Supplemental Tracking: In parallel, studies containing supplemental information (e.g., in vitro assays, toxicokinetic data, New Approach Methodologies - NAMs) are tagged for separate tracking, providing a full panorama of available evidence types [3].

Protocol for Non-Targeted Chemical Analysis (NTA) via High-Resolution Mass Spectrometry

Generating new exposure evidence, a frequent gap identified by SEMs, relies on advanced analytical chemistry. Non-targeted analysis (NTA) using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a key protocol [16].

• Step 1 – Sample Preparation: Extract chemicals from matrices (e.g., water, serum, dust) using solid-phase extraction (SPE). Include internal standards for quality control.
• Step 2 – LC-HRMS Analysis: Chromatographically separate compounds, followed by full-scan MS1 and data-dependent MS2 fragmentation in positive and negative electrospray ionization modes. Use a calibration standard for mass accuracy.
• Step 3 – Data Processing: Process raw files using software (e.g., MS-DIAL, XCMS). Perform peak picking, alignment, and adduct/isotope annotation.
• Step 4 – Compound Annotation: Query generated mass spectra against empirical spectral libraries (e.g., MassBank, NIST) and in-silico fragmentation libraries (e.g., GNPS). Use suspect screening lists (e.g., NORMAN-SLE) for known chemicals of emerging concern. Confidence levels (Level 1-5) are assigned based on the strength of the match [16].
• Step 5 – Prioritization: Prioritize detected but unidentified features (Level 5) based on prevalence, exposure metrics, or link to biological activity via effect-directed analysis (EDA), guiding further structure elucidation efforts [16].

Data Presentation: Characterizing Landscapes and Gaps

The value of an SEM is realized through the systematic presentation of quantitative data that summarizes the evidence base. The following tables exemplify core outputs.

Table 1: Evidence Distribution and Characterization from a Hypothetical SEM on "Chemical X" This table provides a high-level summary of the volume and type of evidence available, immediately highlighting areas of abundance and scarcity.

Evidence Category	Number of Studies	Key Study Characteristics (Examples)	Evidence Strength Indicator
Human Epidemiology	12	Cohort studies (n=8), Case-control (n=4); Outcomes: Liver enzyme elevation (n=7), Thyroid hormones (n=5)	Moderate (consistent findings)
In Vivo Mammalian Toxicology	45	Rodents (n=42), non-rodents (n=3); Exposure duration: Sub-chronic (n=30), Chronic (n=15)	High (extensive testing)
In Vitro / Mechanistic Studies	118	Endpoints: Receptor activation (n=45), Cytotoxicity (n=38), Genotoxicity (n=35)	High (mechanistic clarity)
Environmental Exposure & Fate	25	Matrices: Water (n=15), Soil (n=7), Air (n=3); Regions: North America (n=18), Europe (n=7)	Moderate
Toxicokinetics (ADME)	8	Studies in rats (n=6), in vitro hepatic metabolism (n=2)	Critical Gap
Toxicity to Aquatic Organisms	5	Acute toxicity to daphnia (n=3), fish early-life stage (n=2)	Substantial Gap

Data derived from methodology described in [3]

Table 2: Methodological Comparison of Evidence Synthesis Frameworks This table contrasts SEMs with other review types, clarifying their distinct role in the assessment ecosystem.

Feature	Systematic Evidence Map (SEM)	Systematic Review (SR) for Hazard ID	Traditional Narrative Review
Primary Objective	Characterize evidence extent, distribution, and gaps	Synthesize evidence to answer a focused hazard question	Summarize evidence based on expert selection
Research Question Scope	Broad (e.g., "What evidence exists on chemical X?")	Narrow, specific PECO (e.g., "Does X cause liver toxicity?")	Variable, often broad
Evidence Synthesis	No quantitative synthesis; descriptive summary	Quantitative (meta-analysis) and/or qualitative synthesis required	Selective, qualitative description
Resource Intensity	Moderate to High (broader search, less synthesis)	High (intensive search, appraisal, synthesis)	Low to Moderate
Key Output	Interactive database; visual evidence maps; gap analysis report	Hazard conclusion; confidence rating; dose-response analysis	Scholarly article summarizing current understanding
Regulatory Use Case	Priority-setting; problem formulation; informing SR scoping	Hazard identification; derivation of toxicity reference values	Background context; hypothesis generation
Example Framework	US EPA IRIS SEM Template [3]; CEE Guidelines [8]	Navigation Guide [19]; OHAT Approach [19]	Common in academic journals

Information synthesized from [2] [19] [8]

Visualization of Evidence Landscapes and Relationships

The PECO Framework in Evidence Mapping

The PECO framework structures the research question and eligibility criteria. In an SEM, each element is defined broadly to capture the evidence landscape.

Diagram 3: Broad PECO Framework for Systematic Evidence Mapping

The Scientist's Toolkit: Essential Research Reagents and Materials

The execution of protocols highlighted in this guide, from literature synthesis to laboratory analysis, relies on specialized tools and materials.

Table 3: Research Reagent Solutions for Evidence Mapping and Generation

Item / Solution	Function in SEM/Evidence Generation	Example & Notes
Systematic Review Software	Manages the SEM workflow: reference import, deduplication, dual-screen review, data extraction, and reporting.	DistillerSR, Rayyan, CADIMA. Essential for transparency and reproducibility [3].
Machine Learning Prioritization Tools	Accelerates title/abstract screening by learning from reviewer decisions and ranking remaining records by predicted relevance.	Integrated into SWIFT-Review, Abstractxr. Reduces screening workload by 50-70% [3].
Graph Database Platform	Stores and queries the SEM knowledge graph, allowing for complex, relationship-based exploration of the evidence network.	Neo4j, Amazon Neptune. Enables moving beyond flat tables to interconnected data models [8].
Liquid Chromatography-HRMS System	The core analytical instrument for non-targeted and suspect screening analysis to identify unknown chemicals in exposure assessment.	Orbitrap or Q–TOF mass spectrometers coupled to UHPLC. Provides high mass accuracy and resolution [16].
Solid-Phase Extraction (SPE) Cartridges	Isolate and concentrate a wide range of organic chemicals from complex environmental or biological samples prior to LC-HRMS analysis.	Mixed-mode (C18/SAX/SCX) cartridges are common for broad-spectrum extraction [16].
Chemical Reference Standard Libraries	Essential for confirming the identity of suspected chemicals (Level 1 identification) in non-targeted analysis and for quantification.	Commercial suites (e.g., PFAS, pesticide mixes) and custom-synthesized standards for emerging compounds [16].
Toxico-Ontologies	Controlled, hierarchical vocabularies that provide standardized terms for annotating evidence (e.g., for outcomes, pathways).	The Adverse Outcome Pathway (AOP) ontology; BioAssay Ontology (BAO). Promotes data interoperability [8].

Integration with Regulatory Decision-Making and Future Directions

SEMs are increasingly embedded in regulatory science. The US EPA uses them as a required first step in its IRIS and PPRTV assessments to scope the literature and determine the feasibility and focus of subsequent SRs [3]. In Europe, the Partnership for the Assessment of Risks from Chemicals (PARC) is leveraging SEM-like approaches alongside innovative monitoring to build a next-generation risk assessment paradigm [16] [17].

The 2025 revision of the EU's REACH regulation emphasizes the need for "simpler, faster, bolder" processes [17]. SEMs directly contribute to these goals by enabling rapid evidence surveillance and efficient prioritization of assessment resources. Furthermore, the push for greater transparency through tools like the Digital Product Passport under the EU's Ecodesign Regulation will create new streams of chemical use data that can be integrated into evidence maps [20] [9].

Future advancements will focus on:

• Automation and Living Maps: Integrating natural language processing and machine learning more deeply to create "living" SEMs that continuously update with new publications [8] [3].
• Quantitative Gap Analysis: Moving beyond qualitative gap identification to quantitative models that predict the impact of specific data gaps on risk assessment uncertainty.
• Global Evidence Integration: Linking SEM databases across international regulatory bodies (e.g., EPA, ECHA, OECD) to create a unified global evidence landscape for high-priority substances.

Systematic Evidence Maps represent a fundamental evolution in evidence-based chemical risk assessment. By systematically characterizing broad evidence landscapes and pinpointing critical gaps, they provide an indispensable tool for rational priority-setting, efficient resource allocation, and strategic research planning. Their integration with advanced computational methods like knowledge graphs and machine learning, coupled with cutting-edge analytical protocols for evidence generation, positions SEMs as a cornerstone of a more transparent, agile, and scientifically robust regulatory future. As global chemical production and complexity grow, the role of SEMs in ensuring that risk management decisions are informed by a comprehensive and clear-sighted view of the available science will only become more vital.

In modern chemical risk assessment and research, the volume of scientific literature is vast and growing exponentially. Traditional narrative reviews or narrowly focused systematic reviews, while valuable, often fail to provide the comprehensive, queryable overview required for proactive decision-making in regulatory and research prioritization [15]. Systematic Evidence Maps (SEMs) have emerged as a critical methodology to address this gap. An SEM is defined as a queryable database of systematically gathered research that characterizes broad features of an evidence base, providing a comprehensive summary of large bodies of policy-relevant research [15].

The core function of an SEM is not to perform a full synthesis or meta-analysis, as in a systematic review, but to systematically identify, catalogue, and characterize available evidence. This mapping enables forward-looking predictions, trendspotting, and the efficient identification of evidence clusters and critical gaps [15]. Within the broader thesis on systematic evidence maps in chemical risk assessment research, these tools are foundational. They transform disconnected studies into structured, accessible knowledge assets. The primary outputs of an SEM—interactive databases, tailored visualizations, and detailed evidence inventories—are what deliver its value to researchers, risk assessors, and policy-makers, enabling evidence-based prioritization and hypothesis generation in fields such as toxicology and drug safety [21] [22].

Defining the Core Outputs of a Systematic Evidence Map

The utility of a Systematic Evidence Map is realized through three interconnected, digital-first outputs. Each serves a distinct purpose in making complex evidence bases accessible and actionable.

• Interactive Databases: These are the foundational, structured repositories of all extracted study data. They allow users to query the evidence base using multiple filters (e.g., chemical, outcome, study population, study type) to dynamically retrieve a customized subset of studies. This interactivity moves beyond static PDF tables, empowering users to ask their own questions of the data [23].
• Dynamic Visualizations: These are graphical representations derived from the database. Effective visualizations translate complex metadata and findings into intuitive charts, heat maps, and network diagrams. They are designed not merely for presentation but for exploration, often containing interactive elements that are linked to the underlying database [24] [25].
• Structured Evidence Inventories: This output is a comprehensive, standardized catalogue of all included studies. It typically includes core metadata (e.g., citation, study design), a summary of key elements relevant to the map's scope (e.g., exposure parameters, endpoints measured), and tags or codes applied during the screening process. It serves as the definitive index and source for the other two outputs [15] [21].

The relationship between these outputs is synergistic. The evidence inventory is populated through the systematic review workflow. Its structured data feeds the interactive database, which powers the backend of dynamic visualizations. Users can start their exploration with a visualization to spot a trend, then query the database to see the contributing studies, and finally examine the detailed record for each study in the inventory. This ecosystem transforms a literature collection into an explorable knowledge system.

Diagram 1: The Synergistic Relationship Between Core SEM Outputs. The systematic workflow creates an inventory, which feeds a queryable database that powers visualizations for end-users.

In-Depth Analysis of Core Outputs

Interactive Databases: Architecture and Implementation

An interactive database is the engine of an SEM. Its architecture is designed for flexibility and user autonomy, allowing stakeholders to navigate the evidence without relying on the original research team.

A robust technical architecture follows a layered approach:

• Data Layer: A structured relational (e.g., SQL) or NoSQL database containing all extracted data points from the evidence inventory.
• Application Logic Layer: This layer handles user requests, processes queries, and retrieves data. It is often built using frameworks like R Shiny or Python Dash, or embedded within business intelligence tools like Tableau.
• Presentation Layer: The user interface (UI), typically a web-based dashboard, where users select filters and view results [23].

Key interactive functionalities must include:

• Multi-dimensional Filtering: Allowing users to filter studies simultaneously by chemical, health outcome, study type (in vivo, in vitro, epidemiological), population, and other relevant tags.
• Linked Highlighting: Selecting a study in a results table highlights its position on a corresponding visualization (e.g., a bubble chart), and vice versa.
• Dynamic Search and Export: A keyword search across study metadata and abstracts, with the ability to export filtered results to standard formats (CSV, PDF).

For example, an SEM on inorganic arsenic could allow a user to filter for only in vitro studies that investigated genotoxicity as an endpoint in hepatic cell lines, instantly generating a list of relevant studies and a summary plot [21].

Dynamic Visualizations: Principles of Effective Design

Visualizations translate database queries into intuitive graphics. Beyond simple charts, they must be designed for clarity, accuracy, and inclusivity.

Core Design Principles:

• Perceptual Uniformity: Use color gradients that represent data fairly without visual distortion (e.g., viridis, batlow). Avoid misleading rainbow color maps [26].
• Universal Readability: Ensure visualizations are interpretable by people with color vision deficiencies and remain effective when printed in black and white [26].
• Accessibility Compliance: Adhere to Web Content Accessibility Guidelines (WCAG). For graphical objects and UI components, a minimum contrast ratio of 3:1 against adjacent colors is required. For text within graphics, the contrast ratio must be at least 4.5:1 (or 3:1 for large text) [27] [28].
• Empathetic and Ethical Design: Acknowledge the people behind data points. Avoid aggregation that erases small subgroups; use "near and far" graphics to show both broad trends and individual impacts where possible [24].

Common Visualization Types in SEMs:

• Evidence Heatmaps: Display chemicals on one axis and health outcomes on another, with cells colored by the volume or strength of evidence. This instantly identifies well-studied and data-poor areas.
• Interactive Bubble Charts: Plot studies where bubble position, color, and size encode different dimensions (e.g., study quality, sample size, effect direction).
• Temporal Trend Graphs: Show the accumulation of studies over time, which is useful for identifying emerging research topics.

Diagram 2: User Interaction Workflow with an SEM Dashboard. The process is dynamic and user-driven, from initial filtering to exploration and export.

Structured Evidence Inventories: The Foundational Layer

The evidence inventory is the meticulously curated dataset upon which all other outputs depend. It is the product of a rigorous, protocol-driven screening and data extraction process.

Development Protocol:

• Protocol Registration: Define the research question, search strategy, and inclusion/exclusion criteria a priori.
• Search & De-duplication: Execute searches across multiple databases (e.g., PubMed, Web of Science) and remove duplicates [22].
• Screening: Conduct title/abstract and full-text screening, typically by two independent reviewers.
• Data Extraction & Coding: Extract standardized metadata and content from included studies into a piloted form. This includes tagging studies with relevant modifiers (e.g., "susceptibility factor: age" for an arsenic SEM) [21].
• Quality Assurance: Implement consistency checks and inter-rater reliability assessments.

Content and Structure: A single record in an evidence inventory extends beyond a citation. It is a structured data object containing fields such as:

• Study Identification: DOI, authors, year.
• Study Characteristics: Design (cohort, case-control, in vitro), population/sample, exposure assessment method.
• Intervention/Exposure: Specific chemical, dose, duration.
• Outcomes: Endpoints measured (e.g., cytotoxicity, gene expression, tumor incidence).
• Modifying Factors: Tags for susceptibility factors like genetics, age, or co-exposures [21] [22].
• Results: Key quantitative findings (e.g., points of departure, variability factors) if extraction is quantitative.

This granular, structured data is what enables the powerful filtering and visualization in the downstream outputs.

Case Studies & Quantitative Analysis

Recent applications demonstrate the practical value and quantitative findings generated by SEM outputs in chemical risk assessment.

Table 1: Comparison of Two Recent Systematic Evidence Map Studies in Chemical Risk Assessment

Study Focus	Inorganic Arsenic & Susceptibility [21]	Human Toxicodynamic (TD) Variability [22]
Primary Objective	To map literature on factors modifying susceptibility to iAs exposure.	To map empirical data on human TD variability to assess default uncertainty factors.
Search Yield	Not explicitly stated in abstract.	2,408 studies retrieved from PubMed/Web of Science (2004-2023).
Final Included Studies	Not explicitly stated in abstract.	23 in vitro studies (only 7 provided a quantitative TD variability factor).
Key Gap Identified	Characterization of the distribution and density of evidence on modifiers (e.g., genetics, nutrition).	A severe scarcity of studies designed to isolate and quantify human TD variability.
Impact on Assessment	Provides a clear roadmap for future targeted systematic reviews on specific susceptibility factors.	Suggests the default UF of 3.16 for TD variability is based on extremely limited data, highlighting a critical research need.

Furthermore, regulatory agencies are formally adopting frameworks powered by SEM-like logic. The U.S. FDA's newly proposed Post-Market Assessment Prioritization Tool for food chemicals is a prime example. It employs a Multi-Criteria Decision Analysis (MCDA) approach where chemicals are scored on structured criteria, generating a ranked, evidence-based list for review [29].

Table 2: Criteria from the FDA's Proposed Prioritization Tool (MCDA Framework) [29]

Criterion Category	Specific Criteria Examples
Public Health Criteria	Toxicity (across multiple data types), changes in population exposure, relevance to susceptible subpopulations (e.g., infants), presence of new scientific information.
Other Decisional Criteria	Level of external stakeholder attention, regulatory actions by other agencies (e.g., EU, California), potential impact on public confidence, detection in multiple commodities.

This tool operationalizes the principles of an SEM—systematic gathering and structured scoring of evidence—into a reproducible, transparent regulatory process [29].

The Scientist's Toolkit: Essential Reagents & Software

Creating professional SEM outputs requires a combination of specialized software and adherence to best practice guidelines.

Table 3: Essential Toolkit for Developing Systematic Evidence Map Outputs

Tool Category	Specific Tool / Guideline	Primary Function in SEM Development
Systematic Review Software	DistillerSR, Rayyan, Covidence	Manages the screening process (title/abstract, full-text), facilitates dual review, and maintains an audit trail. Often serves as the initial repository for the evidence inventory [21].
Data Analysis & Visualization	R (with ggplot2, plotly), Python (with Pandas, Matplotlib, Seaborn), Tableau	Performs data wrangling, statistical analysis, and generates static and interactive visualizations. R Shiny and Python Dash are key for building web apps [23].
Dashboard Development	R Shiny, Python Dash, Tableau Public, Power BI	Provides frameworks for building the interactive, web-based dashboard that combines database queries, visualizations, and UI controls into a single application [23].
Color & Accessibility	Scientific Colour Maps (e.g., batlow) [26], WCAG Contrast Checkers [27] [28]	Ensures visualizations are perceptually uniform, accessible to color-blind users, and meet minimum contrast standards for text and graphics.
Style & Reproducibility	Urban Institute Style Guide [25], GitHub, RMarkdown/Jupyter	Promotes consistent, professional styling across charts and supports reproducible research practices through version control and literate programming.

Future Directions & Integration

The future of SEM outputs lies in greater integration, automation, and intelligence. Interoperability between different evidence maps and chemical databases (e.g., EPA's CompTox, ECHA) will create a connected ecosystem of chemical safety evidence. The incorporation of machine learning is advancing rapidly, with models assisting in primary screening (reducing manual workload) and in identifying hidden patterns or predicting novel hazard endpoints across large evidence bases. Furthermore, the line between SEMs and risk assessment is blurring, as seen with the FDA's tool [29]. SEM outputs are evolving from informational resources into direct, decision-support systems that guide resource allocation for both research and regulation. As these tools become more sophisticated and user-friendly, they will be indispensable for navigating the complex evidence landscape of 21st-century chemical risk science.

Building and Using Systematic Evidence Maps: A Step-by-Step Methodology with Case Studies

Systematic Evidence Maps (SEMs) are established as a critical evidence-based tool for informing complex human health assessments within chemical risk assessment research [30]. They function as comprehensive, systematically gathered databases that characterize broad features of an evidence base, providing a visual and queryable overview of available literature [2] [15]. Unlike systematic reviews, which are designed to synthesize data to answer a specific, focused research question, SEMs are optimized for problem formulation and priority-setting [30] [2]. Their primary value lies in scoping the available evidence, identifying critical data gaps, and highlighting clusters of research that may warrant deeper analysis through full systematic review [15]. Within regulatory frameworks like the U.S. Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS) and Provisional Peer-Reviewed Toxicity Value (PPRTV) programs, SEMs are now routinely prepared as integral components of the assessment development process [30]. Their application extends to exploring literature for individual chemicals or groups of chemicals of emerging interest, such as per- and polyfluoroalkyl substances (PFAS) and azo dyes [31] [32], thereby supporting more transparent, efficient, and data-driven decision-making in chemical risk management.

Foundational Protocol Development and Specific Aims

The development of a robust, pre-specified protocol is the cornerstone of a rigorous SEM, ensuring transparency, reproducibility, and reducing the potential for bias [2]. This protocol explicitly defines the project's specific aims and scope.

Defining Specific Aims

The specific aims for an SEM are adaptable but generally encompass a consistent set of core objectives designed to systematically survey and categorize the evidence landscape [30].

Table 1: Standard Specific Aims for a Systematic Evidence Map (SEM)

Aim Category	Description	Example Output
Survey Core Literature	Identify epidemiological (human) and toxicological (mammalian animal) studies reporting health effects, guided by Population, Exposure, Comparator, Outcome (PECO) criteria [30].	Inventory of PECO-relevant studies.
Identify Supplemental Content	Identify and tag studies containing supplemental material (e.g., in vitro, toxicokinetic, non-mammalian, New Approach Methods (NAMs)) not meeting core PECO criteria [30].	Categorized list of supplemental evidence.
Provide Visual Overview	Create interactive literature inventories and visualizations to map the available evidence [30] [31].	Interactive dashboards, evidence maps.
Evaluate Studies (Optional)	Conduct study evaluation (e.g., risk of bias, sensitivity) on PECO-relevant studies, often on a case-by-case basis depending on the SEM's intended use [30].	Quality assessment data.
Summarize Evidence Base	Provide a narrative synthesis describing the volume, distribution, and characteristics of the evidence, highlighting data gaps and evidence clusters [30].	Narrative summary report.

Establishing the PECO Framework

The PECO statement operationalizes the review question and forms the basis for all subsequent search, screening, and inclusion decisions [30] [2]. The criteria are typically kept broad to capture a wide swath of potentially informative literature for human hazard identification [30].

Table 2: Example PECO Criteria for a Chemical Hazard SEM (Adapted from PFAS SEM) [31]

PECO Element	Inclusion Criteria	Exclusion/Supplemental Tagging
Population	Human: Any population/life stage. Animal: Nonhuman mammalian species, any life stage.	Non-mammalian models are tracked as supplemental material [30].
Exposure	Human: Oral or inhalation exposure; biomarkers of exposure. Animal: Oral or inhalation exposure to the specific chemical(s) of interest.	Dermal exposure, injection routes, or mixture-only studies are tagged as supplemental [31].
Comparator	Human: Population with lower/no exposure. Animal: Concurrent vehicle/untreated control group.	Human case reports (1-3 individuals) are tracked as supplemental [31].
Outcome	All health outcomes (cancer and non-cancer).	Studies reporting only exposure data (no health outcome) are supplemental [30].

Core Methodological Workflow: From Search to Extraction

The SEM workflow follows systematic review principles to ensure comprehensive and unbiased evidence collection [30] [31]. The process is highly structured, often utilizing specialized software to manage large volumes of literature.

Literature Search and Screening

A comprehensive search is executed across multiple scientific databases (e.g., PubMed, Web of Science, Scopus) without language or date restrictions to minimize retrieval bias [13] [31]. Searches for hundreds of chemicals can yield over 13,000 records [31]. Screening is typically performed by two independent reviewers to reduce selection bias [30]. Machine-learning software (e.g., SWIFT Active) is increasingly used to prioritize records during title/abstract screening, enhancing efficiency [32]. Studies are screened first against broad PECO criteria at the title/abstract level, then via full-text review [30].

Data Extraction and Categorization

For studies meeting PECO criteria, key data are extracted using structured, web-based forms [30]. Extraction focuses on study design characteristics (e.g., species, sample size, exposure regimen) and health endpoints examined, not on quantitative outcome data for synthesis [30] [31]. Studies are categorized by evidence stream (human, animal) and health system. A critical step is tagging "supplemental material," which includes in vitro studies, mechanistic data, toxicokinetics, and evidence from New Approach Methods (NAMs) [30]. This provides a complete panorama of the available science. Semi-automated data extraction tools (e.g., Dextr) that employ machine learning are under development to improve the scalability of this traditionally manual process [33].

Conducting a modern, large-scale SEM requires a suite of specialized software tools for managing the workflow and data.

Table 3: Research Reagent Solutions: Key Software for SEM Production

Tool Name	Category	Primary Function in SEM Workflow
DistillerSR [13] [32]	Systematic Review Management	Platform for conducting screening (title/abstract, full-text), data extraction, and managing reviewer conflicts.
SWIFT Review/SWIFT Active [32]	Machine Learning / Text Mining	Facilitates prioritization and screening of large literature sets using active learning models.
Dextr [13] [33]	(Semi-)Automated Data Extraction	Web-based tool using machine learning to identify and extract key data fields from study reports, with human-in-the-loop verification.
EPA CompTox Chemicals Dashboard [30]	Chemical Intelligence	Source for chemical identifiers, structures, and properties; used to define the chemical universe for the SEM.
Tableau [13] [32]	Data Visualization	Creates interactive, queryable dashboards and visual evidence maps for public dissemination.

Results Synthesis, Visualization, and Public Dissemination

The results of an SEM are communicated through quantitative summaries, interactive visualizations, and public data sharing, making the evidence base explorable for diverse end-users.

Quantitative Evidence Profiling

The output quantitatively profiles the evidence base, clearly revealing data abundance and gaps. For example, an expanded SEM for 345 PFAS found that over 13,000 studies were identified, but screening yielded only 121 mammalian bioassay and 111 epidemiological studies meeting PECO criteria [31]. Crucially, evidence was available for only 41 PFAS (∼11% of those searched), starkly highlighting the scarcity of traditional hazard data for most chemicals in this large class [31]. Similarly, an SEM on 30 market-relevant azo dyes found 187 relevant studies, with evidence heavily concentrated on just three dyes also used as food additives [32].

Interactive Visualization and Dashboard Creation

A hallmark of contemporary SEMs is the development of interactive, web-based visualizations [30] [31]. Tools like Tableau are used to create queryable literature inventories where users can filter studies by chemical, evidence stream, study design, and health outcome [13] [32]. These dashboards transform the evidence map from a static document into a dynamic scoping and hypothesis-generating tool for researchers and regulators.

Public Dissemination and Data Accessibility

Completing the SEM workflow requires public dissemination of both the findings and the underlying data to ensure transparency and utility [30]. Results are published as peer-reviewed journal articles with detailed methods [31] [32]. Furthermore, interactive dashboards and the extracted metadata are made publicly available online, often in open-access formats [31]. For instance, the EPA compiles results from multiple PFAS SEMs and assessments into a comprehensive public dashboard, providing a centralized resource for the research and regulatory community [31]. This aligns with the broader goal of SEMs to increase the resource efficiency, transparency, and effectiveness of regulatory chemical assessment [2] [15].

In chemical risk assessment, the shift toward evidence-based methodologies has necessitated tools that can efficiently organize and interrogate vast, heterogeneous scientific literature. Systematic Evidence Maps (SEMs) have emerged as a critical problem formulation tool for this purpose. Unlike a systematic review, which synthesizes evidence to answer a narrowly focused question, an SEM provides a queryable database of systematically gathered research to characterize the broader evidence landscape [15]. This allows decision-makers to identify trends, spot evidence gaps, and prioritize areas for future detailed synthesis or primary research [15] [8].

The foundation of any robust SEM is a clearly framed research question. In environmental health and toxicology, the PECO framework (Population, Exposure, Comparator, Outcome) is the established standard for formulating such questions [34]. Formulating broad PECO criteria is particularly crucial for SEMs. The objective is not to restrict inclusion to a specific dose or outcome for synthesis, but to cast a wide net to capture all potentially relevant evidence for mapping and future querying [3]. This breadth ensures the SEM is comprehensive and can serve multiple downstream users with varied information needs, from hazard identification to research trend analysis [8]. Consequently, the process of defining the scope via PECO moves from seeking a single answer to enabling multiple explorations within a curated evidence base.

Defining Broad PECO Criteria for Evidence Mapping

The transition from a PICO (Population, Intervention, Comparator, Outcome) framework, common in clinical research, to PECO reflects the fundamental differences in studying unintentional exposures versus intentional interventions [34]. In SEMs for chemical risk assessment, each PECO component must be defined with inclusive breadth to ensure comprehensive coverage while remaining sufficiently bounded to make the project feasible.

• Population: Criteria should encompass all relevant model systems. For human health, this typically includes human populations and mammalian animal models. A broad SEM scope may also track supplemental evidence from in vitro systems, non-mammalian models, or New Approach Methodologies (NAMs) for context, without making them part of the core PECO [3]. The goal is to capture all data that could inform human health hazard identification.
• Exposure: This is defined by the chemical or chemical group of interest. A broad approach is essential for capturing all relevant exposure scenarios, including different salts, formulations, and environmental metabolites. The exposure metric (e.g., dose, concentration, duration) is recorded during data extraction but is not used as a strict inclusion criterion at the screening stage.
• Comparator: In exposure science, the comparator is often another level of exposure rather than a placebo. For broad scoping, the comparator can be defined as "any comparator," including different doses, other chemicals, or untreated controls. This ensures studies examining dose-response relationships, comparative toxicity, or effects against a background exposure are all captured [34].
• Outcome: Criteria should cover all potential health effects and endpoints. This includes mortality, clinical observations, organ weight changes, histopathology, biochemical markers, and functional assays. A broad outcome scope is vital for identifying unexpected or non-traditional endpoints associated with an exposure.

The guiding principle is to avoid prematurely narrowing the scope based on assumptions about the most important exposure levels or outcomes. The value of the SEM lies in its ability to reveal the evidence distribution across all these dimensions [8].

Paradigmatic PECO Scenarios for Systematic Reviews and Evidence Maps

The formulation of the PECO question can follow different paradigms depending on the state of knowledge and the intended use of the evidence product. The table below outlines five common scenarios, adapting examples from environmental health to a chemical risk context [34].

Table 1: Paradigmatic PECO Scenarios for Evidence Synthesis

Scenario & Context	Analytical Approach	Example PECO Question (Chemical Risk Context)
1. Explore associationLittle known about exposure-outcome relationship.	Explore shape/distribution of relationship.	In adults, what is the effect of a 10 µg/m³ increase in long-term PM2.5 exposure on cardiovascular mortality?
2. Compare exposure extremesCut-offs informed by the reviewed studies.	Use distribution-based cut-offs (e.g., tertiles).	In rodent models, what is the effect of the highest quartile of oral Bisphenol-A exposure compared to the lowest quartile on mammary gland neoplasia?
3. Apply known external cut-offsCut-offs identified from other populations/standards.	Use mean or regulatory cut-offs from external sources.	In manufacturing workers, what is the effect of occupational exposure to lead above the OSHA action level (30 µg/m³) compared to below it on neurobehavioral test scores?
4. Identify protective cut-offsDefine exposure level that ameliorates a known outcome.	Use health-based exposure limits.	In a community, what is the effect of drinking water arsenic concentrations <10 ppb compared to ≥10 ppb on the incidence of skin lesions?
5. Evaluate interventionAssess an action to reduce exposure.	Select comparator based on achievable intervention.	In a population, what is the effect of an in-home water filtration intervention on urinary phthalate metabolite levels compared to no intervention?

For an SEM, the PECO criteria are typically formulated using Scenario 1 (Explore association) as a baseline due to its broad, inclusive nature. The resulting map can then provide the foundational data needed to formulate more specific questions (Scenarios 2-5) for future systematic reviews or risk assessments [34] [15].

Workflow for Developing a Systematic Evidence Map

The development of an SEM follows a structured, transparent workflow to minimize bias and ensure reproducibility. The U.S. EPA’s IRIS and PPRTV programs have standardized a fit-for-purpose methodology that balances rigor with efficiency [3]. The core workflow is visualized in the diagram below and detailed in the subsequent table.

Diagram: Systematic Evidence Map (SEM) Development Workflow

Table 2: Key Steps in the Systematic Evidence Mapping Workflow [3]

Workflow Stage	Key Activities	Methodological Notes for Broad PECO
1. Problem Formulation & PECO	Define objective; establish broad PECO criteria; plan supplemental tracking (e.g., NAMs, ADME).	PECO is kept broad to identify all mammalian bioassay and epidemiological studies informative for human hazard.
2. Protocol Development	Document search strategy, screening process, data extraction forms, and coding taxonomy a priori.	Pre-registration (e.g., on PROSPERO) enhances transparency and reduces risk of bias.
3. Literature Search	Execute structured searches across multiple databases (e.g., PubMed, Embase, ToxLine).	Use broad chemical terms and synonyms; no restrictions on outcome terms; use machine learning for deduplication.
4. Screening	Conduct title/abstract and full-text screening by two independent reviewers.	Inclusion at this stage is based on broad PECO; disagreements are resolved by consensus or third reviewer.
5. Data Extraction & Coding	Extract structured data (study design, population, exposure, outcomes, results) into web-based forms.	Code outcomes to standardized vocabularies (ontologies) to enable grouping and comparison [8].
6. Evidence Database	Compile extracted, coded data into a queryable database or interactive spreadsheet.	Modern approaches use knowledge graphs for flexible storage of connected, heterogeneous data [8].
7. Visualization & Reporting	Generate bubble plots, heat maps, and evidence atlases to show distribution of studies.	Visualizations highlight clusters of research and definitive gaps (e.g., chemical X, outcome Y, model Z).
8. Deliverable	Publish interactive map, full report, and make underlying data publicly accessible.	The final product is a decision-support tool, not a synthesized hazard conclusion.

Quantitative Data Handling and Advanced Data Structures in SEMs

Systematic Evidence Maps involve the handling of large volumes of quantitative metadata (e.g., number of studies, sample sizes, doses) and study results. Effective summarization and presentation of this data are crucial for interpretation.

For continuous data like dosage levels or biomarker concentrations, creating frequency distributions and histograms is a fundamental step. This involves calculating the range of the data, selecting an appropriate number of classes (bins), and determining class widths to clearly display the distribution of exposure levels across the mapped studies [35] [36]. Presenting this graphically allows for immediate identification of the most commonly studied exposure ranges and outliers. Furthermore, basic summary statistics (mean, median, range) for key quantitative variables (e.g., study duration, animal age at exposure) are typically calculated and reported in summary tables [37].

A significant challenge in chemical risk SEMs is managing the heterogeneity and interconnectedness of data (e.g., linking a chemical to its metabolites, multiple toxicity endpoints, and various study models). Traditional flat data tables or spreadsheets can be limiting for this complex data structure [8]. Emerging best practice suggests the use of knowledge graphs as a superior storage and organization model. Knowledge graphs are schemaless, graph-based databases that store entities (e.g., chemicals, outcomes, genes) as nodes and their relationships as edges. This structure is inherently suited for representing the complex networks in toxicological evidence, enabling more powerful and intuitive queries (e.g., "show all studies where Chemical A is associated with Outcome B, mediated by Pathway C") [8]. This approach enhances data integrity, accessibility, and interoperability across different research and regulatory initiatives.

Experimental Protocols and the Scientist's Toolkit

The experimental foundation of an SEM lies in the primary studies it maps. Therefore, understanding common protocols in toxicology is key to designing effective data extraction forms. Below is a detailed protocol for a standard subchronic rodent toxicity study, a core study type frequently encountered in chemical risk evidence maps.

Protocol: OECD Test Guideline 408 - 90-Day Oral Toxicity Study in Rodents

• Objective: To determine the effects of repeated oral exposure to a test chemical over 90 days, identify target organs, and establish a dose-response relationship.
• Test System: Young adult rodents (typically rats, 6-8 weeks old at commencement). Each dose group and control group contains a sufficient number of animals (e.g., 10 per sex) to allow for meaningful statistical analysis.
• Test Article Administration: The chemical is administered daily for 90 days via oral gavage, mixed in a suitable vehicle (e.g., corn oil, methylcellulose). At least three dose levels are used, plus a vehicle control group. The high dose should induce toxicity but not exceed 10% mortality; the low dose should aim to produce no adverse effects (NOAEL).
• In-life Observations: Daily clinical observations for morbidity and mortality. Detailed clinical examinations weekly. Body weight and food consumption measured at least weekly.
• Terminal Procedures: At the end of exposure, hematology and clinical chemistry are analyzed. A full necropsy is performed on all animals. Absolute and relative weights of key organs (liver, kidneys, heart, brain, adrenals, gonads) are recorded. A comprehensive set of tissues is preserved for histopathological examination.
• Data Analysis: Continuous data (body weight, organ weight, clinical chemistry) are analyzed using appropriate parametric (e.g., ANOVA) or non-parametric statistical tests comparing each dose group to the control. Incidence of pathological findings is compared using Fisher's exact or Chi-square tests. The NOAEL and LOAEL (Lowest-Observed-Adverse-Effect Level) are identified.

Research Reagent Solutions and Essential Materials

Table 3: Scientist's Toolkit for Toxicology Studies Mapped in SEMs

Tool/Reagent Category	Specific Examples	Primary Function in Toxicology Research
Animal Models	Sprague-Dawley Rat, CD-1 Mouse, Beagle Dog, Zebrafish (Danio rerio).	In vivo test systems for assessing systemic toxicity, organ-specific effects, and dose-response.
Exposure Vehicles	Corn Oil, Carboxymethylcellulose (CMC), Phosphate-Buffered Saline (PBS), Dimethyl Sulfoxide (DMSO).	Carrier substances for solubilizing or suspending test chemicals for accurate oral, dermal, or injection administration.
Clinical Chemistry Kits	Enzymatic assays for ALT, AST, Creatinine, BUN; ELISA kits for hormones (e.g., T4, Testosterone).	Quantify biomarkers in blood/serum to indicate organ dysfunction (liver, kidney, endocrine).
Histology Supplies	Neutral Buffered Formalin (10%), Hematoxylin and Eosin (H&E) Stain, Paraffin Embedding Systems.	Tissue fixation, processing, staining, and slide preparation for microscopic pathological evaluation.
Molecular Biology Assays	qPCR kits, Western Blot reagents, RNA/DNA extraction kits, ELISA for cytokines (e.g., TNF-α, IL-6).	Investigate mechanistic endpoints: gene expression, protein levels, oxidative stress, inflammation.
Systematic Review Software	DistillerSR, Rayyan, Covidence, EPPI-Reviewer.	Manage the SEM process: reference deduplication, blinded screening, data extraction, and collaboration.
Chemical Databases	EPA CompTox Chemicals Dashboard, PubChem, NLM's TOXNET legacy resources.	Source chemical identifiers, structures, properties, and associated bioactivity data to inform search strategies.

The exponential growth of scientific literature and chemical testing data necessitates advanced informatics approaches for evidence synthesis in risk assessment [38]. Systematic Evidence Maps (SEMs) have emerged as a foundational tool to navigate complex evidence landscapes, identify research trends, and prioritize chemicals and endpoints for deeper analysis [1] [2]. This technical guide details the integration of specialized software and machine learning (ML) into the SEM workflow, positioning it as a critical, efficient precursor to full systematic review within chemical risk assessment [39] [3]. By implementing a structured, semi-automated methodology—encompassing systematic search, screening, and data extraction—researchers can create transparent, queryable evidence databases. These databases support data-driven decision-making for regulatory bodies and efficiently direct resources toward the most pressing human health questions [2] [40].

The Systematic Evidence Map (SEM) Framework in Risk Assessment

A Systematic Evidence Map (SEM) is a structured database of systematically identified and categorized research, designed to characterize the breadth and depth of an evidence base without performing a quantitative synthesis [2]. In chemical risk assessment, SEMs serve as a powerful scoping and prioritization tool, enabling agencies like the U.S. Environmental Protection Agency (EPA) to manage vast numbers of chemicals with limited resources [3]. The core function of an SEM is to visualize research coverage and gaps, answering questions about what evidence exists, for which chemicals and health endpoints, and at what level of study design (e.g., in vivo, in vitro, epidemiological) [1].

The distinction between an SEM and a Systematic Review (SR) is critical. An SR aims to answer a specific, narrow question (e.g., "Does exposure to Chemical X induce liver toxicity in rodents?") through detailed data extraction, critical appraisal, and evidence synthesis to derive a conclusive answer [2]. In contrast, an SEM addresses broader, mapping questions (e.g., "What is the volume and distribution of evidence for hepatotoxicity across a class of 500 PFAS substances?"). It catalogs and describes studies but does not synthesize their results to estimate risk [1] [3]. Thus, SEMs and SRs exist on a methodological continuum, where an SEM efficiently informs the need for and scope of subsequent, more resource-intensive SRs [2].

The EPA has formalized the SEM approach for its Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) programs [3]. Their protocol employs broad Population, Exposure, Comparator, Outcome (PECO) criteria to capture a wide swath of potentially relevant mammalian animal bioassays and epidemiological studies. It also tracks supplemental evidence, including in vitro models, pharmacokinetic data, and New Approach Methodologies (NAMs), providing a comprehensive overview of the available science for a given chemical or chemical group [3].

Software and Machine Learning Integration

The traditional SEM process is resource-intensive, requiring manual screening of thousands of search results. The integration of specialized systematic review software and AI/ML tools is transforming this workflow, dramatically increasing efficiency and consistency [39].

Dedicated systematic review platforms (e.g., DistillerSR, Rayyan, CADIMA) provide structured environments for managing the entire SEM lifecycle. Their core functions include:

• Search Management: De-duplication of records from multiple databases.
• Screening Workflow: Configurable interfaces for title/abstract and full-text screening, often with integrated machine learning prioritization.
• Data Extraction: Customizable forms for consistent coding and data capture.
• Collaboration & Reporting: Tools for dual-independent review, conflict resolution, and audit trail generation [1].

Machine learning, particularly active learning, is now routinely applied to the screening phase. These tools interactively learn from reviewers' decisions to prioritize records likely to be relevant, allowing reviewers to identify most included studies after screening only a fraction of the total search results [39]. The EPA reports using such AI methods to "more efficiently complete resource-intensive tasks like screening literature for relevance and data extraction" [39]. Beyond screening, research is actively exploring the use of generative AI and natural language processing for tasks such as automated data extraction from study reports and document summarization [39].

Table 1: Key Machine Learning Algorithms and Applications in Chemical Risk Assessment

Algorithm Category	Example Algorithms	Primary Application in Risk Assessment	Key Advantage
Traditional Supervised Learning	Random Forest, XGBoost, Support Vector Machines (SVM) [38] [41]	Quantitative Structure-Activity Relationship (QSAR) models, toxicity classification using ToxCast data [41].	High interpretability, robust performance on structured data (e.g., chemical fingerprints, assay results).
Deep Learning	Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs) [38] [40]	Predicting receptor binding, processing image-based toxicology data, modeling complex chemical structures.	Capable of learning from raw, high-dimensional data (e.g., molecular graphs, microscopic images).
Natural Language Processing (NLP)	BERT-based models, other transformer architectures	Document classification during screening, named entity recognition for data extraction from literature.	Automates processing of unstructured text data (scientific abstracts, full-text articles).

A bibliometric analysis of ML in environmental chemical research (1985-2025) confirms the field's rapid growth, with publication output surging from under 25 per year pre-2015 to over 700 in 2024 [38]. The analysis identified eight thematic clusters, with XGBoost and Random Forests as the most cited algorithms, and noted a strong emerging cluster focused directly on risk assessment applications [38].

Experimental Protocols for ML-Enhanced SEMs

Protocol: Developing an ML Model for Toxicity Prediction from High-Throughput Screening Data

This protocol outlines the systematic development of ML models using EPA's ToxCast database, a key resource for Next-Generation Risk Assessment (NGRA) [41].

Objective: To build and select robust, interpretable ML models that predict specific toxicity endpoints from in vitro bioassay data.

Materials & Data Source:

• InvitroDB v4.1: The public ToxCast database containing dose-response data from ~1,500 high-throughput screening assays for thousands of chemicals [41].
• Molecular Fingerprints: Numerical representations of chemical structure (e.g., MACCS, Morgan, RDKit) [41].
• ML Algorithms: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Tree, XGBoost [41].
• NTP ICE Database: Provides annotations linking ToxCast assay targets to regulatory toxicity endpoints (e.g., carcinogenicity, endocrine disruption) [41].

Procedure:

• Data Curation: Extract and preprocess 1,485 bioassay datasets from InvitroDB. Binarize dose-response data into active/inactive calls per chemical per assay.
• Feature-Response Pairing: For each assay, create a dataset where features are the molecular fingerprints of tested chemicals, and the label is the binarized activity call.
• Model Training Pipeline: Systematically train models using all combinations of 5 fingerprint types and 5 ML algorithms for 980 suitable assays, resulting in 24,500 initial models.
• Performance Evaluation: Evaluate each model using the F1 score (harmonic mean of precision and recall) to balance false positives and negatives.
• Model Selection: For each assay, select the single best-performing model (highest F1 score).
• Regulatory Relevance Filtering: Map the assays of the selected models to regulatory endpoints via NTP ICE annotations. Retain only models with F1 score ≥ 0.5 that are relevant to endpoints like acute toxicity, carcinogenicity, or endocrine disruption. This process yielded 311 high-quality, endpoint-relevant models [41].

Significance: This systematic approach generates a curated toolbox of validated models. These models can be used within an SEM to prioritize chemicals for further testing based on predicted bioactivity or to help categorize and interpret in vitro evidence mapped from the literature [41].

Protocol: Executing a Semi-Automated Systematic Evidence Map

This protocol details the end-to-end workflow for creating an SEM for a chemical risk assessment topic, incorporating software and ML tools.

Objective: To comprehensively identify, screen, and categorize all relevant scientific literature on a defined set of chemicals and health outcomes.

Materials: Systematic review software (e.g., DistillerSR), bibliographic databases (PubMed, Web of Science, Embase, etc.), access to full-text articles.

Procedure:

• Problem Formulation & Protocol: Define the SEM's scope using a broad PECO statement. Publish a pre-defined, publicly available protocol detailing search strategy, inclusion/exclusion criteria, and data extraction categories [3].
• Systematic Search: Execute the comprehensive search strategy across multiple databases. Import all results into the systematic review software for de-duplication.
• AI-Assisted Screening:
  • Title/Abstract Screening: Use the software's ML module to prioritize records. Reviewers screen the prioritized list, marking records as "Include," "Exclude," or "Uncertain." The ML model continuously learns from these decisions to refine prioritization.
  • Full-Text Retrieval & Screening: Retrieve full-text documents for all records not excluded. Apply eligibility criteria against the full text.
• Data Extraction & Coding: For each included study, extract metadata and study characteristics into a structured form. Key data points include chemical studied, study system (human, animal, in vitro), health endpoint examined, and exposure information [3].
• Study Evaluation (Optional): Depending on the SEM's purpose, a risk of bias assessment may be conducted on a subset of studies (e.g., all in vivo studies) [3].
• Visualization & Reporting: Generate interactive evidence maps, heatmaps, and summary tables from the coded database. The final report describes methods, presents visualizations, and discusses evidence clusters and gaps [1].

Diagram 1: SEM Workflow with AI Integration

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Essential Digital Tools & Data Resources for ML-Enhanced SEMs

Tool/Resource Name	Category	Primary Function in SEM	Key Features & Relevance
DistillerSR	Systematic Review Software	Manages the entire SEM workflow from search to reporting.	Implements AI prioritization for screening, ensures audit trail, facilitates dual review; used by major agencies [1].
EPA ToxCast (InvitroDB)	Toxicology Database	Provides high-throughput screening data for ML model development and hypothesis generation.	Contains thousands of assay endpoints; essential for building predictive models for NGRA [41].
VOSviewer / R Bibliometrix	Bibliometric Analysis Software	Analyzes trends, clusters, and gaps in the scientific literature itself.	Used to create co-occurrence and citation networks; helps map the research landscape as part of problem formulation [38].
RDKit	Cheminformatics Toolkit	Generates molecular fingerprints and descriptors for QSAR/ML modeling.	Converts chemical structures into numerical features usable by ML algorithms; foundational for computational toxicology [41].
XGBoost / Scikit-learn	Machine Learning Library	Provides algorithms for building classification and regression models.	Offers state-of-the-art, interpretable algorithms (like Random Forest, XGBoost) commonly used in toxicology prediction [38] [41].

Data Synthesis and Visualization

The power of an SEM is realized through the synthesis and visualization of extracted data. This moves beyond narrative lists to interactive, spatial representations of the evidence base.

Evidence Heatmaps are a central visualization tool. A typical heatmap displays chemicals on one axis and health endpoints or study types on the other. The cells are color-coded to represent the volume of evidence (e.g., number of studies) or the level of confidence (e.g., presence of a high-quality in vivo study). This instantly reveals which chemical-endpoint pairs are well-studied and which are evidence deserts [1].

Network Diagrams can show relationships between chemicals, molecular targets, and outcomes, particularly useful when integrating omics or ToxCast bioactivity data [40]. Interactive online dashboards allow users to filter, sort, and drill down into the underlying data, transforming the SEM from a static report into a dynamic decision-support tool [1].

Table 3: Statistical Overview of ML in Environmental Chemical Research (2015-2025)

Metric	Value / Finding	Implication for SEMs
Publication Growth (2024)	>719 publications in 2024 [38]	Field is rapidly expanding, increasing the volume of literature SEMs must handle.
Leading Algorithm Popularity	XGBoost and Random Forests most cited [38]	These robust, interpretable models are preferred for regulatory-facing prediction tasks.
Health vs. Environmental Focus	4:1 bias in keyword frequency toward environmental over human health endpoints [38]	Highlights a critical evidence gap that SEMs can help identify and prioritize for filling.
EPA's Reported Efficiency Gain	AI tools "dramatically reduce the time" for screening and extraction [39]	Justifies investment in and integration of these tools to scale up SEM production.

Diagram 2: ML and Data Integration Pipeline

Future Perspectives and Challenges

The future of SEMs in chemical risk assessment lies in greater automation, integration, and intelligence. Generative AI holds promise for automating more complex tasks, such as drafting study summaries or extracting specific numerical data points from text and tables [39]. The integration of adverse outcome pathway (AOP) frameworks into SEMs could allow for the mechanistic organization of evidence, linking chemical bioactivity to key events and apical outcomes [40]. Furthermore, the development of "living" SEMs—continuously updated evidence maps—would provide a sustainable solution for evidence surveillance in a fast-paced field [2].

Significant challenges remain. Methodological standardization is needed to ensure consistency and reliability across different SEM projects [1]. The interpretability and transparency of ML models are paramount for regulatory acceptance; "black box" models are insufficient [40]. There is also a need to balance the efficiency of automation with the rigor of human expert judgment, particularly in complex study evaluation tasks. Addressing these challenges through collaborative efforts between toxicologists, data scientists, and regulators will be essential to fully realize the potential of systematic searching and screening to inform public health protection.

Systematic Evidence Maps (SEMs) have emerged as a critical evidence synthesis tool within chemical risk assessment, designed to navigate increasingly complex and voluminous scientific landscapes [2]. Unlike systematic reviews, which aim for definitive answers to narrowly focused questions, SEMs provide a comprehensive, queryable overview of a broad evidence base [2]. Their primary function is to categorize and organize scientific evidence, identifying overarching trends, clusters of research, and critical knowledge gaps [1]. For regulators and researchers contending with tens of thousands of chemicals in commerce, SEMs offer a strategic, resource-efficient approach to prioritizing assessment efforts and guiding future research or targeted systematic reviews [2] [3]. By structuring extracted data into interoperable formats, SEMs transform scattered literature into a structured knowledge base that supports transparent, evidence-informed decision-making in environmental health and drug safety [1].

Foundational Methodologies: From Protocol to Visualization

The construction of a reliable SEM follows a rigorous, standardized workflow to ensure transparency, reproducibility, and utility. The process begins with defining a broad scope and a Population-Exposure-Comparator-Outcome (PECO) statement, which is intentionally kept wider than that of a systematic review to capture the full evidence landscape [3]. This is followed by a comprehensive literature search across multiple databases to minimize retrieval bias [2].

A critical, resource-intensive phase is the dual-step screening of titles/abstracts and full texts against predefined eligibility criteria, often conducted by two independent reviewers to reduce selection bias [3]. Subsequently, the core task of structured data extraction and coding commences. Data from included studies are extracted using standardized forms, capturing details on study design, chemical, exposure scenario, model system, and health outcomes [1] [3]. This coding process structures the evidence into machine-readable fields, enabling future querying and analysis. The final stages involve critical appraisal (on a case-by-case basis), data synthesis through narrative summaries, and visualization using tools like heatmaps and interactive databases to reveal patterns [1] [2]. The entire workflow is typically preregistered in a protocol to safeguard against methodological bias [2].

Table 1: Comparative Analysis: Systematic Evidence Maps vs. Systematic Reviews

Feature	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Objective	Map the breadth of evidence; identify trends, clusters, and gaps [1] [2].	Synthesize evidence to answer a specific, focused question [2].
Research Question Scope	Broad (e.g., "What evidence exists on the health effects of chemical X?") [3].	Narrow and specific, defined by a detailed PECO [2].
Data Synthesis	Categorization and narrative summary; no quantitative meta-analysis [1].	Quantitative (meta-analysis) and/or qualitative synthesis of results [2].
Critical Appraisal	Conducted selectively or to categorize studies; not always required [1] [3].	Mandatory component to evaluate risk of bias and weight of evidence [2].
Output	Interactive database, visual maps (heatmaps, network diagrams), gap analysis report [1].	A definitive conclusion or effect estimate with a confidence rating [2].
Regulatory Utility	Problem formulation, priority setting, guiding targeted SRs or primary research [2] [3].	Directly informs risk assessment decisions and derivation of toxicity values [2].

Technical Core: Data Extraction, Coding, and Interoperability

The transformative power of an SEM lies in its structured, coded data framework. Extraction moves beyond simple bibliographic details to capture key study elements relevant to risk assessment. For a chemical SEM, this includes variables such as chemical identifier (e.g., CASRN), study type (e.g., mammalian bioassay, epidemiology, in vitro), exposure pathway and duration, health system examined, and outcomes measured [3]. This process creates a standardized evidence inventory where each study is tagged with multiple descriptive codes.

The goal is interoperability—structuring data so it can be seamlessly queried, filtered, and connected with other datasets. Coded data is typically stored in relational databases or structured formats (e.g., JSON, XML), enabling users to ask complex questions: "Show all chronic inhalation studies on chemical X reporting neurological outcomes in rodents." This structure is fundamental for generating interactive visualizations and for linking evidence to other knowledge systems, such as exposure databases or adverse outcome pathways (AOPs) [42].

To address the scalability challenge of manual extraction, machine learning (ML) and automation are being integrated into the workflow. Proof-of-concept projects use semi-automated tools (e.g., "Dextr") where ML models pre-populate extraction fields from full-text articles, which are then verified by a human reviewer—a "human-in-the-loop" approach [33]. This hybrid method promises significant efficiency gains while maintaining the accuracy required for regulatory science [33].

Experimental Protocol: Implementing an SEM for Chemical Hazard Assessment

The following protocol, adapted from the US EPA template [3] and recent methodological guidance [1], outlines key experimental steps for generating an SEM in chemical risk assessment.

Protocol Title: Systematic Evidence Map for Health Effects of [Chemical Name/Chemical Class].

Objective: To systematically identify, catalogue, and characterize the available mammalian and epidemiological literature on the health effects of [Chemical] to inform hazard assessment and research prioritization.

1. Protocol Registration & Scope Definition:

• Preregister the SEM methodology on a public registry (e.g., PROSPERO, Open Science Framework).
• Define the broad PECO:
  • Population: Human populations (for epidemiological studies) or mammalian animal models (e.g., in vivo rodent studies).
  • Exposure: [Chemical/Chemical Class] via any relevant route (oral, inhalation, dermal).
  • Comparator: Control or reference exposure group.
  • Outcome: Any health-related endpoint or toxicological effect [3].

2. Information Sources & Search Strategy:

• Databases: Systematically search PubMed/MEDLINE, Embase, Scopus, Web of Science, and TOXLINE.
• Search Strategy: Develop a sensitive search string using chemical identifiers (names, CASRN) and broad health terms. Avoid overly restrictive outcome terms. The strategy is peer-reviewed using the PRESS checklist.
• Supplementary Searching: Screen reference lists of relevant reviews and regulatory reports.

3. Study Screening & Selection:

• Tool: Use systematic review software (e.g., DistillerSR, Rayyan, SWIFT-Review) to manage the process.
• Process: Conduct a two-phase screening.
  • Phase 1 (Title/Abstract): Two independent reviewers screen records against broad eligibility criteria (e.g., original research, relevant chemical, mammalian system). Conflicts are resolved by consensus or a third reviewer [3].
  • Phase 2 (Full Text): Two independent reviewers assess the full text of potentially relevant studies against the final PECO criteria. Reasons for exclusion are documented [1].

4. Data Extraction & Coding (Core Experimental Step):

• Tool: Use a standardized, web-based extraction form in a platform like DistillerSR or the EPA's Dextr tool [33].
• Pilot Testing: Pilot the extraction form on 5-10 studies and refine it to ensure consistency.
• Variables to Extract:
  • Citation & Study ID
  • Chemical & Exposure: Chemical name(s), CASRN, dose/conc., route, duration.
  • Study Design: Model system (species, strain, cell line), sample size, study type (bioassay, cohort, etc.), funding source.
  • Outcomes: Health system(s) investigated (e.g., hepatic, neurological, developmental), specific endpoints measured, direction of effect (if reported) [3].
  • Supplementary Data Flag: Indicate if the study contains data on ADME (Absorption, Distribution, Metabolism, Excretion), genotoxicity, or New Approach Methodologies (NAMs) [3].
• Process: Two extractors independently extract data from a subset of studies (e.g., 10-20%) to calibrate and ensure reliability. The remainder may be extracted by one reviewer and verified by a second [3].

5. Study Evaluation & Data Visualization:

• Risk of Bias/Study Evaluation: Apply a tool like the OHAT Risk of Bias Rating tool selectively, based on the intended use of the SEM [3].
• Data Synthesis & Mapping: Code extracted data into categories. Use visualization software (e.g., EPPI-Mapper, Tableau, R Shiny) to create:
  • Heatmaps: Display the volume of evidence across chemical-health outcome pairs.
  • Interactive Evidence Databases: Allow users to filter and query the underlying data [1].
  • Flow Diagrams: Document the study screening process (PRISMA style).

Table 2: Key Metrics and Outputs from an SEM Protocol Implementation

Metric Category	Specific Measures	Typical Output Target/Example
Search Yield	Number of records identified from databases & other sources.	5,000 - 15,000+ records for a broad chemical query [3].
Screening Efficiency	Percentage of records excluded at Title/Abstract vs. Full-Text stage.	~85-95% excluded at T/A; ~50% of remaining excluded at FT [1].
Final Included Studies	Total number of studies meeting PECO criteria.	Varies widely; defines the scope of the mapped evidence base.
Data Extraction Consistency	Inter-rater reliability (e.g., Cohen's Kappa) on pilot extraction.	Kappa > 0.8 indicates excellent agreement [3].
Evidence Distribution	Count of studies by health outcome category, study type, or model system.	e.g., "Liver toxicity: 45 studies (30 rodent, 10 in vitro, 5 human)."
Gap Identification	Areas with zero or very few studies given high priority.	e.g., "No chronic low-dose inhalation studies on developmental effects."

Visualizing the Workflow: From Data to Decision

The following diagrams illustrate the core SEM development process and the integrated data extraction pipeline.

Systematic Evidence Map Development Workflow

Integrated Data Extraction and Coding Pipeline

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 3: Research Reagent Solutions for SEM Implementation

Tool Category	Specific Tool/Resource	Function in SEM Process
Project Management & Screening	DistillerSR, Rayyan, SWIFT-Review, Covidence	Manages the systematic review workflow: deduplication, dual-reviewer screening, and decision tracking [3] [33].
Data Extraction & Curation	Custom web-based forms (e.g., in DistillerSR), EPA Dextr tool, REDCap	Provides structured, electronic forms for consistent data extraction. Dextr incorporates ML for semi-automation [33].
Chemical & Hazard Data	EPA CompTox Chemicals Dashboard, PubChem, OECD QSAR Toolbox	Provides authoritative chemical identifiers, properties, and curated data to standardize chemical information in coding [42].
Visualization & Analysis	EPPI-Mapper, Tableau, R (ggplot2, Shiny), Python (Matplotlib, Plotly)	Generates evidence heatmaps, interactive dashboards, and network diagrams from coded data [1] [43].
Evidence Inventory Platforms	Health Assessment Workspace Collaborative (HAWC), Systematic Review Data Repository (SRDR+)	Hosts and disseminates interactive, publicly accessible evidence maps and extracted data [1].
Machine Learning / AI	NLP models for text classification (e.g., in Dextr), Zotero with AI plugins	Automates aspects of screening and data extraction, increasing efficiency in the "human-in-the-loop" model [33].

Data extraction and coding form the analytical backbone of the Systematic Evidence Map, transforming unstructured literature into a structured, interoperable knowledge asset. By adhering to rigorous, transparent methodologies and leveraging emerging tools in machine learning and data visualization, SEMs provide an indispensable strategic overview for chemical risk assessment [1] [2]. They enable regulators and scientists to efficiently prioritize assessments, justify research investments, and ensure that subsequent, more resource-intensive systematic reviews are focused on decision-critical questions [3]. As the field evolves, the integration of automated extraction and linked data principles will further enhance the scalability, speed, and utility of SEMs, solidifying their role as a foundational tool for evidence-informed toxicology and public health protection [33].

Within the domain of chemical risk assessment, researchers and regulators are tasked with navigating an expansive, complex, and rapidly growing evidence base. Evidence Gap Maps (EGMs) and Systematic Evidence Maps (SEMs) have emerged as critical tools to address this challenge [2]. These tools are defined as systematic, visual presentations of the availability of relevant evidence for a particular policy or research domain [44]. Unlike a systematic review, which synthesizes findings to answer a specific question about effectiveness, an EGM aims to chart the existing landscape of evidence—categorizing studies by interventions (or exposures), outcomes, populations, and study designs—to graphically highlight both clusters of research and critical knowledge gaps [45] [46].

In chemical risk assessment, this methodology is invaluable. Regulatory frameworks like EU REACH and US TSCA require decisions on thousands of substances, often with heterogeneous and patchy toxicological data [2]. An SEM provides a comprehensive, queryable overview of this broad evidence base, enabling the identification of trends, the prioritization of chemicals for full systematic review, and the strategic planning of future primary research to fill decisive gaps [2]. By transforming a dispersed body of literature into an interactive visual tool, EGMs enhance transparency, reduce bias in evidence selection, and serve as a foundational resource for evidence-informed decision-making (EIDM) in both policy and research prioritization [45] [47].

Core Methodological Framework for EGM Development

The development of a rigorous EGM follows a structured, multi-step process analogous to systematic review but with distinct objectives focused on mapping rather than synthesis. The following protocol, synthesized from contemporary guidance, details each essential phase [45] [48].

Table 1: Core Methodological Steps for Developing an Evidence Gap Map in Chemical Risk Assessment

Step	Key Activities & Objectives	Chemical Risk Assessment Application Example
1. Define Scope & Protocol	Formulate broad research question; establish PECO/PICO framework; develop and publish an a priori protocol.	Question: “What is the extent and nature of in vivo and in vitro evidence on the endocrine-disrupting potential of phthalates?”
2. Systematic Search	Design comprehensive, multi-database search strategy; include published/unpublished literature; document search strings.	Search PubMed, TOXLINE, Embase, and regulatory dossiers for phthalates AND (endocrine disruption OR receptor binding OR reproductive toxicity).
3. Screening & Selection	Apply pre-defined inclusion/exclusion criteria via dual-independent screening (title/abstract, then full-text).	Include primary studies measuring endocrine-sensitive endpoints; exclude review articles, non-peer-reviewed reports.
4. Data Extraction & Coding	Extract high-level data (study design, chemical, model system, outcomes measured) into a structured framework.	Code each study for: phthalate congener, dose, exposure window, test system (species/cell line), specific outcome (e.g., serum testosterone, gene expression).
5. Critical Appraisal (Optional)	Assess risk of bias if map intends to characterize quality or inform subsequent synthesis.	Apply tools like OHAT risk of bias rating to animal studies for selection, performance, detection, and attrition biases.
6. Data Visualization & Mapping	Populate the EGM matrix (interventions/exposures vs. outcomes); use interactive platforms for presentation.	Create a matrix where rows are phthalates, columns are health outcomes (e.g., male reproductive, female reproductive, metabolic); cells indicate volume and study design of evidence.
7. Interpretation & Reporting	Describe evidence landscape, identify dense areas and gaps, discuss implications for research and policy.	Report heavy clustering of evidence on DEHP and male reproduction, with severe gaps for newer substitutes and neurodevelopmental outcomes.

Detailed Experimental Protocols

Protocol 1: Defining the Conceptual Framework The initial step requires developing a PECO statement (Population, Exposure, Comparator, Outcome) tailored for environmental health [2]. For chemical risk, this becomes: Population (e.g., experimental models: rodents, zebrafish, human cell lines), Exposure (specific chemical or class, dose/duration), Comparator (control or alternative exposure), and Outcomes (toxicological endpoints: mortality, organ weight, histopathology, molecular biomarkers). Engaging stakeholders (e.g., regulators, toxicologists) at this stage ensures the framework aligns with decision-making needs [46] [47].

Protocol 2: Executing the Systematic Search A replicable, broad search strategy is constructed. This involves consulting multiple bibliographic databases (PubMed, Scopus, Web of Science, TOXLINE) and grey literature sources (EPA reports, EFSA opinions). Search strings combine chemical terms (e.g., “Bisphenol A”, “flame retardants”) with outcome terms (e.g., “carcinogenicity”, “developmental toxicity”) and study type filters. The search process must be documented meticulously to ensure transparency and reproducibility [45] [2].

Protocol 3: Data Extraction and Categorization Standardized extraction forms are used to capture metadata from each included study. Key fields include citation, study design (e.g., randomized controlled trial, cohort, in vitro), exposure details, outcome measures, and model characteristics. This information is coded according to the a priori framework. Specialized software (e.g., EPPI-Reviewer, Rayyan) is highly recommended to manage this process for large evidence bases [45] [46].

Protocol 4: Constructing the Interactive Map The coded data is used to populate a two-dimensional matrix. The visual representation is then built using interactive visualization tools. Platforms like the 3ie EGM tool or specialized JavaScript libraries (e.g., D3.js) allow users to filter the map by chemical, outcome, study design, or risk of bias, and to click on matrix cells to retrieve the underlying study citations and details [46].

EGM Development Workflow (Max 760px width)

Visualization and Interactive Presentation

The power of an EGM lies in its visual and interactive components, which transform complex data into an accessible format for exploration.

The Core Matrix Visualization: The primary diagram is typically a two-dimensional heatmap or bubble plot. In chemical risk, one axis often represents chemical exposures or classes, while the other represents health outcomes or toxicological endpoints [2]. Each cell in the matrix visualizes the volume and type of available evidence (e.g., number of studies, proportion of high-quality studies) using color gradients or bubble sizes. Cells with no evidence remain empty, making gaps immediately apparent.

Interactivity and User Engagement: Modern EGMs are built as web-based interactive tools [46] [47]. Key interactive features include:

• Drill-down filtering: Allowing users to filter the map to show only studies meeting specific criteria (e.g., only in vivo studies, only studies on a specific species).
• Linked evidence: Clicking on a populated cell reveals the list of underlying studies, often with direct links to abstracts or full texts.
• Dynamic updating: The platform architecture should permit the incorporation of new studies as they are published, supporting the concept of a “living map” [2].

Evidence Synthesis Ecosystem (Max 760px width)

Table 2: Research Reagent Solutions for Evidence Gap Mapping

Tool Category	Specific Tool/Resource	Primary Function in EGM Development
Project Management & Deduplication	Rayyan, Covidence	Facilitates collaborative title/abstract and full-text screening among review team members; helps remove duplicate records.
Systematic Review Software	EPPI-Reviewer, DistillerSR	Comprehensive platforms supporting the entire workflow: screening, data extraction, coding, risk of bias assessment, and basic matrix creation.
Data Visualization & Interactivity	3ie EGM Platform, Tableau, R (ggplot2, plotly), D3.js	Specialized tools for creating the interactive two-dimensional matrix visualization and hosting the final online, searchable map.
Chemical-Specific Databases	PubMed, TOXLINE, EPA HERO, ICE	Essential bibliographic databases for comprehensive retrieval of toxicological and environmental health literature.
Methodological Guidance	Campbell Collaboration, CEE Guidelines, PRISMA-ScR	Provide standardized protocols and reporting checklists to ensure methodological rigor and transparency in the mapping process.

Application in Chemical Risk Assessment Research

Within the thesis context of systematic evidence maps for chemical risk assessment, EGMs serve several pivotal functions [2]:

• Evidence Prioritization and Triage: Regulatory agencies can use EGMs to rapidly assess the breadth and depth of evidence on hundreds of chemicals, identifying which substances have sufficient data to warrant a full systematic review and which are “data-poor” and require targeted testing [2].
• Strategic Primary Research Planning: By clearly identifying gaps—such as a lack of data on a specific chemical-outcome pair or a reliance on outdated study designs—EGMs direct academia and industry towards the most critical research questions, reducing wasteful duplication [47].
• Informing Mechanistic and New Approach Methodologies (NAMs): An EGM can reveal clusters of evidence around certain adverse outcome pathways (AOPs). This can guide the development and validation of in vitro or in silico NAMs for those pathways, where traditional data is abundant enough to serve as a benchmark.

For example, an EGM on per- and polyfluoroalkyl substances (PFAS) could visually demonstrate a heavy concentration of epidemiological and toxicological studies on liver toxicity and cholesterol, but a stark absence of high-quality evidence on neurodevelopmental or immunotoxic effects. This gap map would directly inform research agencies to fund studies on these underrepresented outcomes and guide regulators to apply higher uncertainty factors when assessing risks for those endpoints.

The integration of automation and machine learning is the next frontier for EGMs in this field. Natural language processing algorithms can assist in screening titles and abstracts and extracting PECO data, dramatically increasing the efficiency of updating maps to keep pace with the explosive growth of the toxicological literature [45]. This evolution will solidify the EGM as an indispensable, living tool for navigating the complex evidence landscape of chemical risk.

Systematic Evidence Maps: The Foundational Framework for Modern Risk Assessment

Systematic Evidence Maps (SEMs) represent a critical evolution in the methodology of human health risk assessment, particularly within the US Environmental Protection Agency’s (EPA) Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) programs [3]. These tools are designed to provide a structured, transparent, and reproducible overview of the available scientific literature for a chemical or group of chemicals. Their primary function is to serve as a problem formulation tool, helping to scope the breadth of evidence, identify key data gaps, and establish assessment priorities [3]. Within the context of a broader thesis on systematic approaches in chemical risk assessment, SEMs are not intended to conduct quantitative dose-response analysis or derive toxicity values directly. Instead, they systematically catalog the evidence landscape, enabling assessors to make informed decisions about which chemicals or health endpoints warrant a full systematic review or where new research is most urgently needed [3].

The methodology for developing an SEM follows a standardized, protocol-driven workflow to ensure consistency and objectivity [3]. The process begins with defining broad PECO criteria (Populations, Exposures, Comparators, and Outcomes) to capture mammalian animal bioassays and epidemiological studies relevant to human hazard identification [3]. A key feature of the EPA’s SEM approach is the tracking of both PECO-relevant studies and supplemental content. This supplemental tracking includes data from in vitro models, non-mammalian systems, exposure-only studies, pharmacokinetic models, and New Approach Methodologies (NAMs) like high-throughput screening and in silico models [3]. The use of specialized software and machine learning tools facilitates the efficient screening of vast literatures, with critical steps like full-text review and data extraction typically performed by two independent reviewers to minimize bias [3]. The final output is an interactive, visual representation of the evidence base, allowing users to filter and explore data by chemical, study type, health outcome, and other key variables [3].

Table: Summary of Finalized EPA IRIS PFAS Assessments (as of 2025)

PFAS Compound	Final Assessment Date	Key Health Effects Identified	Critical Study Types
Perfluorohexanoic Acid (PFHxA)	April 2023 [49]	Liver, developmental, immunological effects [50]	Animal chronic/cancer bioassays, epidemiological studies [3]
Perfluorohexanesulfonic Acid (PFHxS)	January 2025 [49] [51]	Thyroid, liver, kidney, developmental, immunological effects [51]	Animal toxicology, human cross-sectional & cohort studies [51]
Perfluorodecanoic Acid (PFDA)	July 2024 [49]	Hepatic, endocrine, developmental effects [50]	Mammalian bioassays, in vitro mechanistic data [50]
Perfluorononanoic Acid (PFNA)	Final Review Completed (Sep 2024) [49]	Developmental, hepatic, serum lipid effects [50]	Animal developmental studies, human biomarker data [50]

Experimental and Assessment Methodologies in Detail

2.1 Protocol for a Systematic Evidence Map on PFAS The experimental protocol for an SEM, as exemplified by the work for IRIS assessments, is a multi-stage process [3].

• Protocol Development: A detailed, publicly posted protocol defines the assessment objectives, PECO criteria, search strategy, and inclusion/exclusion rules. For example, EPA released a systematic review protocol for PFHxS in November 2019 and an updated version for several PFAS in July 2020 [51].
• Search & Screening: Comprehensive searches are executed across multiple scientific databases (e.g., PubMed, Web of Science). Title/abstract and subsequent full-text screening are performed against the PECO criteria using tools like machine learning classifiers and review management software, with dual independent review [3].
• Data Extraction & Categorization: Data from included studies are extracted into structured forms. Key elements include chemical, study design (human, animal in vivo, in vitro), exposure regimen, health system examined (e.g., hepatic, endocrine), and outcomes [3]. Studies are also tagged for supplemental information like ADME (Absorption, Distribution, Metabolism, and Excretion) data or genotoxicity [3].
• Study Evaluation & Mapping: A preliminary evaluation of study utility or reliability may be conducted. All extracted data are then visualized in an interactive evidence map, often as a sortable database or heatmap, showing the density and distribution of studies across chemicals and health endpoints [3].

2.2 Quantitative Risk Assessment for PFAS Following an SEM, a full toxicological review for a priority chemical like PFHxS employs rigorous quantitative risk analysis [51]. This involves:

• Dose-Response Analysis: Identifying pivotal studies that report critical effect levels. Benchmark dose (BMD) modeling is often applied to animal toxicology data to determine a point of departure (POD), such as a BMDL (Benchmark Dose Lower Confidence Limit) [49] [51].
• Uncertainty Factor Application: The POD is divided by composite uncertainty factors (UFs) to derive a reference dose (RfD) or reference concentration (RfC). These UFs account for interspecies differences, human variability, database deficiencies, and duration extrapolation [51].
• Probabilistic & Sensitivity Analyses: Advanced assessments may use Monte Carlo simulations to model the probability distribution of risk across a population, accounting for variability in exposure and susceptibility [52]. Sensitivity analysis is used to identify which input variables (e.g., exposure duration, specific toxicity value) have the greatest influence on the risk estimate, guiding data collection and refining the assessment [52] [53].

Visualizing Molecular and Systemic Pathways

The toxicity of PFAS, such as PFHxS, is mediated through specific molecular initiating events that cascade into adverse outcomes. A primary pathway involves the activation of peroxisome proliferator-activated receptors (PPARs), particularly PPARα. PFAS compounds act as ligands for these nuclear receptors [50]. The diagram below outlines this canonical pathway and its systemic effects, which underpin the non-cancer health effects—like hepatic steatosis, altered lipid metabolism, and developmental toxicity—identified in IRIS assessments [50] [51].

The Scientist's Toolkit: Research Reagent Solutions

Table: Key Reagents and Materials for PFAS Toxicology Research

Item	Function in PFAS Research	Application Example
Analytical Standards (Neat & Isotope-Labeled PFAS)	Quantitative calibration and tracing of PFAS in biological/environmental matrices; essential for exposure biomonitoring and ADME studies [50].	Measuring serum PFHxS levels in epidemiological cohorts or tracking distribution in rodent models [51].
PPAR-Responsive Reporter Assay Kits	In vitro screening to identify PFAS as agonists/antagonists of PPAR isoforms, a key molecular initiating event [50].	High-throughput screening of PFAS mixtures for PPARα activation potential [3].
Liver Enzyme & Lipid Profile Assay Kits	Measure biomarkers of hepatotoxicity (ALT, AST) and dyslipidemia (cholesterol, triglycerides) in serum or tissue homogenates [51].	Assessing hepatic effects in animal studies used for IRIS dose-response analysis [51].
Cytokine Multiplex Panels	Profile immune markers to evaluate the immunosuppressive effects of PFAS exposure [50] [51].	Investigating altered immune function in in vivo studies or ex vivo cell cultures.
New Approach Methodologies (NAMs)	Includes high-throughput transcriptomics (e.g., TempO-Seq), computational toxicology models, and defined cell cultures to reduce animal testing and explore mechanisms [3].	Building mechanistic evidence for PFAS categories and screening data-poor PFAS [50] [3].

Systematic evidence maps (SEMs) represent a transformative methodological advancement in chemical risk assessment, designed to address the critical challenge of evidence surveillance and priority-setting within regulatory and research frameworks. Unlike systematic reviews, which aim to synthesize evidence to answer a specific, tightly focused research question, SEMs function as comprehensive, queryable databases that characterize the broad landscape of available research on a given chemical or class of chemicals [2]. Their primary utility lies in providing a transparent, evidence-based overview that supports forward-looking predictions, trend-spotting, and the efficient identification of knowledge clusters and critical gaps [2].

Within the broader thesis of chemical risk assessment research, SEMs serve as a foundational problem-formulation and scoping tool. Regulatory bodies, including the U.S. Environmental Protection Agency (EPA), now routinely employ SEMs in programs like the Integrated Risk Information System (IRIS) and the Provisional Peer Reviewed Toxicity Value (PPRTV) program [3] [54]. Their application extends from informing data gaps and determining the need for updated assessments to prioritizing which chemicals or health endpoints warrant a full, resource-intensive systematic review [54]. In an era defined by a constant influx of new scientific literature and new approach methodologies (NAMs), SEMs offer a structured, reproducible mechanism for continuously evaluating new evidence and determining which updates are most critical for protecting human health and the environment.

Regulatory and Methodological Evolution: The Imperative for Efficient Evidence Evaluation

The evolution of chemical regulatory policy underscores the necessity for tools like SEMs. Recent developments under the U.S. Toxic Substances Control Act (TSCA) highlight a drive toward more efficient, fit-for-purpose risk evaluations. In September 2025, the EPA proposed amendments to its risk evaluation process, seeking to tailor the scope and level of analysis to what is needed to make a decision on a specific chemical [55] [12]. A key proposal is to return to making separate risk determinations for each condition of use (e.g., industrial processing, consumer use), rather than a single chemical-wide determination [12]. This shift demands a more nuanced understanding of the evidence base for specific exposure scenarios, a task for which SEMs are ideally suited.

Concurrently, agencies like the U.S. Food and Drug Administration (FDA) are developing transparent, science-based methods for prioritizing chemicals for post-market assessment. The FDA's proposed method employs Multi-Criteria Decision Analysis (MCDA) to rank chemicals based on hazard, exposure, and public concern, emphasizing the need for systematic approaches to triage assessment resources [56]. These regulatory movements create a pressing demand for methodologies that can rapidly and systematically survey vast evidence landscapes, identify new data, and facilitate decisions on where to focus limited assessment resources. SEMs meet this demand by providing a structured, auditable process for evidence characterization that aligns with principles of regulatory transparency and scientific defensibility [2] [3].

Table 1: Comparative Functions of Systematic Evidence Maps (SEMs) and Systematic Reviews (SRs) in Risk Assessment

Feature	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Objective	To catalog, characterize, and visualize the extent, distribution, and key features of an evidence base [2].	To synthesize evidence to answer a specific question, producing a quantitative or qualitative summary estimate of effect [2].
Research Question	Broad; aims to identify all literature meeting broad PECO criteria for a chemical/class [3].	Narrow and focused; uses a precise PECO statement [2].
Evidence Synthesis	Does not perform synthesis or meta-analysis; data is extracted for descriptive characterization [2].	Core function; involves quantitative or qualitative synthesis of results from included studies [2].
Output	Interactive database, evidence atlas, gap analysis, priority-setting report [3] [54].	Narrative report with synthesized findings, confidence ratings, and direct conclusions [2].
Ideal Use Case	Problem formulation, assessment prioritization, evidence surveillance, informing the need for an SR [2] [54].	Hazard identification, dose-response analysis, deriving toxicity values for risk assessment [2].

Methodological Core: The SEM Development Workflow

The development of a robust SEM follows a standardized, multi-phase workflow that ensures comprehensiveness, transparency, and reproducibility. The U.S. EPA's template for IRIS and PPRTV assessments provides a detailed methodological blueprint [3]. The process is designed to be systematic yet adaptable, allowing for "fit-for-purpose" adjustments based on the specific assessment context.

Phase 1: Protocol Development and Problem Formulation The process begins with defining the assessment objective and developing a pre-published protocol. A broad Population, Exposure, Comparator, Outcome (PECO) statement is established to guide the search. For example, a SEM on a chemical may seek to identify all mammalian bioassay and epidemiological studies investigating any health outcome [3]. The protocol also defines the scope of supplemental information to be tracked, such as in vitro studies, pharmacokinetic data, and evidence from New Approach Methodologies (NAMs) [3].

Phase 2: Comprehensive Search and Screening A comprehensive search strategy is executed across multiple scientific databases. To manage the potentially large volume of records, the workflow often incorporates machine learning software and automated tools for initial screening [3]. The screening is typically performed by two independent reviewers to minimize error and bias. Records are sequentially screened by title/abstract and then by full text against the eligibility criteria [2].

Phase 3: Data Extraction and Characterization Studies that meet the PECO criteria undergo structured data extraction. Key study design elements (e.g., test species, exposure regimen, health systems examined) are captured using web-based extraction forms [3]. This step does not extract detailed numerical results for synthesis but rather descriptive data that allows for the characterization and categorization of the evidence. The extracted data is stored in a relational database designed for querying and visualization.

Phase 4: Study Evaluation and Visualization Formal risk-of-bias evaluation may be conducted on a case-by-case basis depending on the SEM's purpose [3]. The final output is a publicly accessible, interactive evidence map. Data can be visualized through dashboards that allow users to filter evidence by study type, health outcome, exposure scenario, or other extracted variables. This facilitates gap analysis and trend identification [3] [54].

Table 2: Key Phases in the Systematic Evidence Map Workflow [3]

Phase	Key Activities	Tools & Outputs
1. Planning & Scoping	Define objective; develop broad PECO; write and publish protocol.	Protocol document; stakeholder input.
2. Search & Screening	Execute multi-database search; de-duplicate records; title/abstract and full-text screening.	Bibliographic software (e.g., DistillerSR, Rayyan); machine learning classifiers; screened library of studies.
3. Data Extraction	Extract descriptive data from included studies using structured forms.	Custom web-based extraction forms; relational database.
4. Evaluation & Visualization	Conduct study evaluation (if needed); develop interactive visualizations and reports.	Evidence dashboard (e.g., Tableau, R Shiny); gap analysis report; priority-setting recommendations.

A Framework for Prioritizing Assessment Updates Using SEMs

The true power of SEMs is realized when they are deployed as dynamic tools for continuous evidence surveillance and prioritization. The following framework outlines a systematic process for using SEMs to evaluate new evidence and decide when a formal assessment update is warranted.

Step 1: Establish a Living SEM Baseline The process begins with an existing, published SEM that serves as the definitive baseline snapshot of the evidence for a chemical or topic. This baseline SEM is housed in a platform that allows for the addition of new records.

Step 2: Implement Proactive Evidence Surveillance A structured surveillance strategy is established to periodically (e.g., quarterly) search for newly published literature. This involves running updated search queries in scientific databases using the original SEM search strategy, filtered for recent dates. Automated alerts and feeds from key journals can supplement this process.

Step 3: Integrate and Triage New Evidence Newly identified records are screened against the original PECO criteria. Those that are included are extracted and added to the SEM database. A triage analysis is then performed to characterize the new evidence. This involves answering key questions: Does the new evidence fill a previously identified critical data gap? Does it pertain to a high-priority health outcome or susceptible population? Does it introduce a new, higher-quality study type (e.g., a new epidemiological cohort) that could change confidence in existing findings?

Step 4: Apply a Multi-Criteria Decision Analysis (MCDA) for Prioritization Inspired by methods used by the FDA and EPA [56], an MCDA framework is used to score and rank the need for an assessment update. New evidence is evaluated against pre-defined, weighted criteria. A scoring rubric transforms qualitative judgments into quantitative scores to support transparent decision-making.

Table 3: Example MCDA Criteria for Prioritizing Assessment Updates

Criterion	Description	Weight	Scoring Example (0-3)
1. Fills Critical Data Gap	Does the new evidence address a key uncertainty previously identified as critical for risk assessment?	High	3=Fills a major gap in potency or mode-of-action data.
2. Relevance to Susceptible Populations	Does the evidence inform risk for a potentially exposed or susceptible subpopulation (e.g., children, pregnant women)?	High	3=Provides direct data on a sensitive subpopulation.
3. Strength & Quality of New Evidence	What is the reliability and robustness of the new study designs (e.g., human vs. animal, guideline vs. exploratory)?	Medium	3=High-quality epidemiological study or robust guideline-compliant animal bioassay.
4. Potential to Alter Risk Conclusions	Could the new evidence, if credible, change the previous hazard identification or dose-response conclusion?	High	3=Evidence suggests a new, more serious health endpoint or a lower potency.
5. Public & Regulatory Concern	Is there heightened stakeholder interest or regulatory attention on this endpoint or chemical?	Medium	3=Chemical/endpoint is the subject of significant public petition or regulatory action in another jurisdiction.
Total Score	Weighted sum of all criteria scores.		Threshold score triggers recommendation for full systematic review update.

Step 5: Decision and Resource Allocation The output of the MCDA is a ranked list of chemicals or endpoints where new evidence most strongly justifies a comprehensive reassessment. This enables assessment bodies to allocate resources to full systematic reviews or revised risk evaluations where they will have the greatest impact on public health protection [54]. For other chemicals, the updated SEM itself serves as the record of evidence surveillance, providing assurance that the assessment remains current until a higher-priority trigger is met.

Experimental Protocols for Integrating New Evidence into an Existing SEM

Protocol for Periodic Evidence Surveillance Update

• Objective: To identify, screen, and integrate newly available evidence into an existing Systematic Evidence Map for the purpose of surveillance and update prioritization.
• Search Update Execution:
  • Run the original, peer-reviewed SEM search strategy in all previously used databases (e.g., PubMed, Embase, Scopus, TOXLINE).
  • Apply a date filter to retrieve records published after the date of the last search performed for the baseline SEM.
  • Execute searches on a pre-defined schedule (e.g., every 3 or 6 months).
  • Combine results, remove duplicates using bibliographic software.
• Screening Against Original PECO:
  • Upload the new record set into the SEM's project file within systematic review software (e.g., DistillerSR, Rayyan).
  • Apply the original, unchanged PECO-based eligibility criteria.
  • Conduct title/abstract screening, followed by full-text retrieval and screening. A minimum of two independent reviewers shall perform screening, with conflicts resolved by a third senior reviewer.
• Data Extraction and Integration:
  • For studies meeting inclusion criteria, perform data extraction using the same structured form and variables as the baseline SEM.
  • Append the newly extracted data to the master SEM database, ensuring new records are tagged with a "surveillance update" identifier and the date of integration.
• Triage and Characterization Report:
  • Generate a summary report characterizing the volume and nature of new evidence (e.g., number of new studies by type, health outcome, and study quality).
  • Highlight studies that address previously noted evidence gaps or involve susceptible populations.
  • Present findings to the assessment team for consideration in the MCDA prioritization process.

Protocol for Conducting a Prioritization Multi-Criteria Decision Analysis (MCDA)

• Convene Prioritization Panel: Assemble a multidisciplinary panel of 5-7 experts in toxicology, epidemiology, risk assessment, and data science.
• Calibrate Criteria and Weights: Present the panel with the proposed MCDA criteria (Table 3). Using a modified Delphi process or direct weighting exercise, finalize the criteria and their relative weights to reflect programmatic priorities.
• Score New Evidence: For each chemical/endpoint with new evidence, the panel reviews the triage characterization report. Panelists independently score the evidence against each criterion using the defined rubric.
• Calculate Aggregate Scores: Collect individual scores, calculate median scores for each criterion, and compute the total weighted score for each chemical/endpoint.
• Deliberate and Recommend: The panel meets to review the ranked list. Discussion focuses on areas of scoring divergence, the rationale for high-scoring items, and final recommendations. The output is a formal recommendation document listing which topics are recommended for a full systematic review update, which require only minor assessment addenda, and which require no immediate action.

Table 4: Key Research Reagent Solutions for SEM Development

Item / Reagent	Function in SEM Process	Example / Note
Systematic Review Software Platform	Manages the entire SEM workflow: reference import, de-duplication, multi-level screening, data extraction, and sometimes analysis. Ensures audit trail and reviewer coordination.	DistillerSR, Rayyan, CADIMA, EPPI-Reviewer.
Machine Learning (ML) Classifiers	Accelerates title/abstract screening by learning from human reviewer decisions and prioritizing records most likely to be relevant. Dramatically reduces manual screening burden [3].	Integrations within DistillerSR, Rayyan; ASReview, SWIFT-Review.
PECO Criteria Framework	The foundational scaffold that defines the scope of the SEM. A broad but well-defined PECO ensures the map is comprehensive and fit-for-purpose [3].	Example: Population (Humans, Mammalian animals); Exposure (Chemical X); Comparator (Lower/No exposure); Outcome (Any health effect).
Structured Data Extraction Form	A customized digital form used to capture descriptive data from each included study consistently (e.g., study design, species, exposure route, outcomes measured).	Built within systematic review software or as a web form linked to a database (e.g., REDCap). Critical for generating visualizations.
Interactive Data Visualization Dashboard	Transforms the extracted database into an accessible, filterable interface for exploring the evidence landscape. Essential for gap analysis and communication.	Developed using business intelligence tools (Tableau, Power BI) or open-source frameworks (R Shiny, plotly in Python).
Bibliographic Database APIs	Enable programmable, reproducible execution of complex search strategies across multiple databases, facilitating regular surveillance updates.	PubMed E-utilities, Elsevier Scopus API, Wiley Web of Science API.
Chemical Identification Resolver	Standardizes chemical names, synonyms, and identifiers (CAS RN) across the literature search and data extraction process, ensuring comprehensive retrieval.	NIH NLM Chemical Identifier Resolver (CIR), EPA CompTox Chemicals Dashboard.

Diagram 1: Systematic Evidence Map (SEM) Workflow and Evidence Flow

Diagram 2: Prioritization Framework for Assessment Updates Using SEMs

Diagram 3: Evidence Integration and Decision Pathway for New Data

Overcoming Challenges in Evidence Mapping: Best Practices and Technological Solutions

Systematic evidence maps (SEMs) have emerged as a critical tool for evidence-based decision-making in chemical risk assessment, operating within a landscape defined by regulatory pressure and expanding evidence bases [2]. These maps provide a comprehensive, queryable summary of broad research fields, characterizing the extent, type, and features of available evidence without performing a full evidence synthesis [2]. Within frameworks like the U.S. Environmental Protection Agency’s Integrated Risk Information System (IRIS), SEMs are employed to identify data gaps, inform assessment priorities, determine the need for updated evaluations, and act as problem formulation tools to refine future systematic review questions [54] [57].

The adoption of SEMs represents a strategic response to the inherent limitations of systematic reviews (SRs) in regulatory contexts. While SRs offer a gold standard for synthesizing evidence on focused questions, their time and resource intensity often clash with the pace and scope of regulatory decision-making, which must manage legacy chemicals, evaluate new substances, and integrate diverse data types [2]. SEMs offer a more resource-efficient first step, creating a transparent and structured overview of the literature. This process enhances the credibility of assessments and facilitates coordination across different programs and agencies by providing a common, shared starting point for analysis [57].

However, the development of robust SEMs is not without significant methodological challenges. This guide examines three core pitfalls that threaten the utility and integrity of SEMs in chemical risk assessment: managing resource intensity, navigating subjective coding during data extraction, and maintaining objectivity throughout the process. Addressing these pitfalls is essential for producing SEMs that are scientifically defensible, operationally feasible, and capable of supporting high-stakes regulatory and public health decisions.

Pitfall 1: Resource Intensity in SEM Development

The systematic and comprehensive nature of SEMs, while a core strength, introduces significant demands on time, personnel, and financial resources. This intensity can be a major barrier to implementation, particularly for regulatory bodies and research groups facing constrained budgets and tight deadlines for chemical evaluations [2].

Quantitative Scope and Comparative Demands

The resource burden of an SEM is directly proportional to the scope of the research question and the volume of identified literature. A broad chemical assessment can easily yield thousands of potentially relevant citations for screening. The following table contrasts the procedural stages and resource implications of SEMs with traditional Systematic Reviews (SRs), highlighting where SEMs offer relative efficiencies and where demands remain high [2].

Table 1: Comparative Resource Requirements: Systematic Evidence Maps vs. Systematic Reviews

Procedural Stage	Systematic Evidence Map (SEM)	Systematic Review (SR)	Key Resource Implications
Protocol & Question	Broad, mapping-focused. May use Population, Exposure, Comparator, Outcome (PECO) elements flexibly to capture wide evidence base [2].	Narrow, synthesis-focused. Uses strict PECO framework to define a specific answerable question [2].	SEM protocol development may be quicker due to broader focus, but search strategy design is complex due to wide scope.
Search & Retrieval	Comprehensive search across multiple databases to capture all relevant evidence on a topic [2].	Comprehensive search focused on a precise question [2].	Comparably High. Both require extensive, peer-reviewed search strategies. SEM searches may yield larger initial result sets.
Screening	Title/abstract and full-text screening against broad eligibility criteria.	Title/abstract and full-text screening against strict eligibility criteria.	High for SEM. Larger literature yield and broader criteria can make SEM screening more voluminous and time-consuming.
Data Extraction	Extracts descriptive, bibliographic, and study design characteristics (e.g., population, exposure, outcome type). Does not typically extract detailed quantitative results for meta-analysis [2] [57].	Extracts detailed data on study methods, results, and risk of bias to enable synthesis and effect size calculation [2].	Lower for SEM. The absence of deep results extraction and critical appraisal for synthesis reduces time and expertise required per study.
Critical Appraisal	May catalog reported methodological aspects but does not formally weight studies or exclude based on quality for the map itself [57].	Formal risk-of-bias assessment for each included study is mandatory and influences synthesis and conclusions [2].	Lower for SEM. Eliminating formal appraisal significantly reduces resource burden.
Output	Interactive databases, structured tables, and visualizations depicting the landscape of evidence (e.g., evidence clusters, gaps) [2] [57].	Qualitative or quantitative synthesis (e.g., meta-analysis), GRADE assessment, and narrative conclusions [2].	SEM output focuses on visualization and characterization, avoiding the highly specialized analytical work of synthesis.

Experimental Protocol: Streamlined Screening and Extraction Workflow

To mitigate resource intensity without compromising systematic rigor, a standardized, efficient workflow is essential. The following protocol outlines key steps for the most resource-heavy phases: screening and data extraction.

Protocol Title: High-Throughput Screening and Extraction for Systematic Evidence Mapping

Objective: To efficiently identify and characterize relevant studies from a large bibliographic dataset using a structured, multi-phase process.

Materials & Software: Bibliographic reference management software (e.g., EndNote, Rayyan), structured data extraction forms (e.g., built in Microsoft Excel, Google Sheets, or specialized tools like EPPI-Reviewer), and inter-rater reliability calculation tools.

Procedure:

• Search Result De-duplication: Use automated tools within reference management software to remove duplicate records from multiple database searches. Perform a manual check to validate automated results.
• Pilot-Tested Screening Form:
  • Develop a clear, unambiguous screening form based on the broad eligibility criteria (e.g., chemical of interest, any health outcome, any study design).
  • Pilot Phase: A minimum of two reviewers independently screen the same random sample of 50-100 titles/abstracts.
  • Calculate inter-rater reliability (e.g., Cohen’s Kappa). A Kappa ≥ 0.6 indicates acceptable agreement. If lower, refine criteria and form until acceptable agreement is achieved [58].
• Primary Screening (Title/Abstract):
  • Reviewers screen the de-duplicated list using the piloted form.
  • Use a “liberal acceleration” approach: any reviewer marks a study as “include,” it moves to the next stage. Only studies marked “exclude” by all reviewers are removed.
  • Conflicts (e.g., one include, one exclude) are flagged for automatic advancement to full-text review.
• Secondary Screening (Full-Text):
  • Retrieve full texts of all studies advanced from primary screening.
  • Two reviewers independently assess full texts against eligibility criteria.
  • All exclusion decisions require consensus. A third reviewer adjudicates unresolved disagreements.
• Structured Data Extraction:
  • Develop and pilot a standardized extraction form to capture predefined descriptive fields (e.g., publication year, study design in vivo, in vitro, human observational), chemical, exposure pathway, outcome category, population/model system).
  • For large SEMs, use a “single extractor with verification” model: one reviewer performs primary extraction, a second reviewer verifies a random sample (e.g., 20%) of extracted records for accuracy and consistency.
• Data Validation & Cleaning: Perform systematic checks for internal consistency (e.g., date ranges, categorical variables) before analysis and visualization.

Visualization: SEM Development Workflow

The following diagram illustrates the sequential workflow for developing a systematic evidence map, highlighting stages of high resource demand and key decision points for efficiency.

Pitfall 2: Subjectivity and Inconsistency in Coding

Data extraction in SEMs involves coding study characteristics into predefined categories—a process inherently vulnerable to subjective interpretation. Inconsistent coding compromises the reliability, queryability, and comparability of the final evidence map, undermining its value for decision-making [58].

Thematic Analysis Frameworks and Coding Approaches

The field of qualitative research, particularly thematic analysis (TA), provides a relevant framework for understanding coding subjectivity. TA is not a single method but a family of approaches with different epistemological foundations that directly impact how coding consistency is viewed and managed [58].

Table 2: Approaches to Coding and Their Implications for Objectivity in SEMs

Coding Approach	Epistemological Foundation	View on Researcher Subjectivity	Procedures for Consistency	Applicability to SEM Data Extraction
Coding Reliability TA [58]	(Post)positivist. Values accuracy, reliability, and minimizing "bias." [58]	A threat to be controlled and minimized.	Use of structured codebooks, multiple independent coders, calculation of intercoder agreement (ICA) metrics (e.g., Cohen’s Kappa), consensus coding. [58]	High. Suitable for extracting objective, descriptive study characteristics (e.g., study design, species, outcome domain) where high consistency is required.
Reflexive TA [58]	Big Q, interpretative. Views knowledge as situated and partial [58].	A necessary resource for deep interpretation. "Researcher bias" is a positivist concept that is rejected [58].	Emphasizes researcher reflexivity, organic code development, and themes as meaning-based stories. Does not seek or measure intercoder agreement. [58]	Low. Not appropriate for primary descriptive extraction in SEMs. May inform later, higher-order interpretation of mapped patterns.
Codebook TA (e.g., Framework Analysis) [58]	Hybrid. Combines structured procedures with qualitative values [58].	Acknowledged and managed through team discussion and structured process.	Often starts with a preliminary codebook, refined iteratively. May use multiple coders with discussion to converge on meaning, but may not calculate formal ICA [58].	Moderate to High. Useful for more complex categorization where some interpretation is needed (e.g., coding "exposure scenario" from text). Relies on team consensus.

A common pitfall is methodological incoherence—unknowingly mixing procedures from different approaches, such as using a reflexive, organic coding style but then calculating intercoder agreement, which assumes a fixed, measurable "accuracy" [58]. For SEMs, adopting a Coding Reliability or structured Codebook approach is most appropriate for the core data extraction tasks to ensure the map is a reliable resource.

Experimental Protocol: Ensuring Coding Reliability

This protocol details steps to maximize consistency and minimize subjective drift during the coding (data extraction) phase of an SEM.

Protocol Title: Establishing and Maintaining Coding Reliability for Descriptive Data Extraction

Objective: To achieve and document high levels of agreement among coders extracting descriptive data from studies included in an SEM.

Materials & Software: Detailed codebook with definitions and examples, piloted data extraction form, statistical software for calculating intercoder agreement (e.g., SPSS, R, or online calculators).

Procedure:

• Codebook Development:
  • Create a comprehensive codebook before extraction begins. For each variable (field) to be extracted, provide:
    • A clear conceptual definition.
    • A list of all possible categorical values.
    • Decision rules for ambiguous cases.
    • Example excerpts from studies for each value.
• Coder Training:
  • Train all coders simultaneously using the codebook and sample studies not included in the SEM.
  • Discuss examples and clarify decision rules until all coders demonstrate understanding.
• Pilot Reliability Testing:
  • Select a random sample of 10-15 studies from the SEM inclusion list.
  • All coders independently extract data from the pilot sample using the codebook and form.
  • Calculate Intercoder Agreement: Use Cohen’s Kappa (κ) for categorical variables and Intraclass Correlation Coefficient (ICC) for continuous variables. Analyze agreement per variable, not just overall.
  • Threshold: Aim for κ ≥ 0.70 for key categorical variables, indicating substantial agreement. ICC ≥ 0.75 indicates good reliability.
• Consensus Meeting & Codebook Refinement:
  • Review all pilot extractions item-by-item. Discuss all discrepancies to understand their source.
  • Refine the codebook definitions and decision rules to resolve ambiguities.
• Independent Primary Coding:
  • Divide the full set of studies among coders for independent extraction using the finalized codebook.
• Ongoing Verification:
  • Implement a quality control check: a second coder independently re-extracts a randomly selected subset (e.g., 10-20%) of the primary coder’s studies.
  • Re-calculate agreement metrics periodically. If agreement drops, pause and retrain coders or further refine the codebook.

Visualization: Coding Approach and Objectivity Relationship

The following diagram maps the relationship between different coding methodologies, their underlying philosophies on researcher subjectivity, and the corresponding strategies for achieving rigor in an SEM context.

Pitfall 3: Conceptualizing and Maintaining Objectivity

The pursuit of "objectivity" is paramount in regulatory science, yet its meaning is often contested, especially when integrating diverse forms of evidence. In the context of SEMs, a rigid, value-neutral concept of objectivity is unattainable and can be counterproductive. The pitfall lies in failing to articulate and implement a robust, defensible model of objectivity appropriate for systematic mapping [59].

From Value-Neutrality to Strong Objectivity

Traditional, positivist-influenced views equate objectivity with value-neutrality—the elimination of researcher perspective to reveal a single, mind-independent truth. This "weak objectivity" often masks the influence of dominant perspectives and fails to scrutinize its own starting assumptions [59].

A more robust framework for SEMs is "strong objectivity" [59]. This approach:

• Acknowledges Situatedness: Recognizes that all researchers and research processes are situated within specific social, historical, and disciplinary contexts that shape their perspectives [59].
• Requires Reflexivity: Demands critical self-examination by researchers of their own assumptions, values, and potential influences on the research process (e.g., choice of mapping categories, interpretation of ambiguous study descriptions) [59].
• Actively Seeks Diverse Vantage Points: Maximizes objectivity by structurally incorporating multiple perspectives. In chemical assessment, this means explicitly seeking and valuing different types of evidence (e.g., in vitro new approach methodologies alongside traditional in vivo studies) and considering the relevance of findings to different populations or exposure scenarios [59].

For an SEM, strong objectivity is operationalized not by pretending the map is a perfect mirror of reality, but by making the mapping process as transparent and systematic as possible, documenting all decisions (e.g., in a publicly available protocol), and subjecting the process to peer review or stakeholder feedback.

Experimental Protocol: Integrating Reflexivity for Strong Objectivity

This protocol provides a structured method for integrating reflexivity—a core tenet of strong objectivity—into the SEM team process.

Protocol Title: Structured Reflexivity Exercises for SEM Teams

Objective: To explicitly identify, document, and mitigate the influence of team assumptions and perspectives on key decision points in the SEM process.

Materials: Reflexivity log (shared document), guided question prompts, facilitator for team discussions.

Procedure:

• Pre-Work Reflexivity (During Protocol Development):
  • Individual Exercise: Each team member privately answers prompts in a reflexivity log:
    • What are my prior research experiences or beliefs about the chemical or health outcomes in question?
    • What types of evidence (e.g., experimental vs. epidemiological) do I intuitively weigh more heavily and why?
    • What are my assumptions about the goals of this map (e.g., is it primarily for identifying research gaps or for prioritizing chemicals for regulation)?
  • Team Discussion: Convene a meeting to share anonymized insights. Discuss how these perspectives might influence choices in defining search boundaries, developing eligibility criteria, or designing data extraction categories. Document agreed-upon mitigation strategies (e.g., consulting a broader advisory group on scope).
• Mid-Process Reflexivity (During Screening/Extraction):
  • Checkpoint Meetings: After processing a batch of studies (e.g., every 200 screened), hold brief team huddles.
  • Guided Prompt: "Have we encountered any studies that challenged our initial assumptions about the evidence base? Are we applying eligibility criteria consistently, or are we making 'exceptions' based on unstated preferences?"
  • Update the codebook or screening guidelines based on insights to ensure consistency moving forward.
• Interpretive Reflexivity (During Analysis/Visualization):
  • When analyzing patterns in the mapped data (e.g., "80% of studies are in vitro"), the team explicitly discusses:
    • Alternative Explanations: Is this a true feature of the science, or an artifact of our search strategy (e.g., excluding non-English studies, specific databases)?
    • Framing Effects: How do different visualization choices (e.g., a treemap vs. a bar chart) highlight or obscure certain patterns [60] [61]?
    • Audience Impact: How might different stakeholder groups (regulators, industry scientists, community advocates) interpret this map differently? [61]
• Documentation: The key insights and methodological adjustments from all reflexivity exercises are summarized in a dedicated section of the final SEM report, demonstrating a commitment to transparency and strong objectivity.

Constructing a rigorous, objective, and efficient systematic evidence map requires a suite of methodological and technological tools. The following table details key resources for navigating the common pitfalls discussed.

Table 3: Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Specific Item/Resource	Function & Relevance to Pitfalls	Example/Note
Protocol & Project Management	Pre-published, registered protocol (e.g., on Open Science Framework).	Mitigates resource intensity by forcing upfront planning and reducing ad-hoc decisions. Enhances objectivity via transparency.	Required for high-quality SEMs [2].
Systematic Review Software	Dedicated platforms (e.g., EPPI-Reviewer, Covidence, Rayyan).	Manages resource intensity by streamlining de-duplication, screening, and collaboration. Enables coding reliability through built-in dual-screening and conflict resolution features.	Often cloud-based, facilitating team collaboration across institutions.
Coding Framework	Structured codebook with definitions, decision rules, and examples.	The primary tool against subjective coding. Standardizes extraction to ensure consistency and reliability across coders [58].	Should be developed iteratively and piloted before full use.
Intercoder Agreement Metrics	Statistical measures (Cohen’s Kappa, ICC).	Quantifies coding subjectivity and provides a measurable benchmark for coder training and reliability [58].	Kappa ≥ 0.7 is a common target for substantial agreement.
Reflexivity Log	Shared document with guided prompts.	Operationalizes strong objectivity by making team assumptions and decision-points explicit and open to scrutiny [59].	Should be maintained throughout the project lifecycle.
Data Visualization Platforms	Interactive tools (e.g., Tableau, R Shiny, Microsoft Power BI).	Transforms extracted data into accessible maps, helping to manage resource intensity by making the product usable for multiple downstream purposes [57]. Critical for clear communication.	Enables creation of interactive, filterable evidence databases for end-users [60] [61].
Reporting Templates	Standardized SEM report templates (e.g., from EPA IRIS program).	Promotes harmonization, reduces resource intensity for report writing, and enhances objectivity through comprehensive, structured reporting [54] [57].	Using a community-accepted template improves comparability across maps.

Chemical risk assessment is fundamentally a challenge of connecting disparate data points. Researchers must link chemical structures to toxicological outcomes, map exposure pathways to population health effects, and trace mechanistic evidence across biological scales. Traditional flat-table database architectures, while excellent for structured, uniform data, struggle with this interconnected reality. They force complex biological and chemical relationships into rigid schemas, creating data silos that obscure critical patterns and necessitate cumbersome joins for even basic relationship queries [62].

This structural limitation becomes particularly problematic within the framework of Systematic Evidence Maps (SEMs), which are increasingly deployed to organize complex evidence landscapes in environmental health [3] [1]. SEMs aim to categorize vast scientific literature to identify trends and knowledge gaps, a process that inherently involves mapping relationships between chemicals, study designs, health endpoints, and evidence streams. When constrained by relational tables, this mapping becomes a logistical bottleneck, slowing down the evidence synthesis crucial for regulatory decisions and public health protection.

The shift to flexible knowledge graphs represents a paradigm change tailored to this domain. A knowledge graph structures information as a network of entities (nodes) and their relationships (edges), mirroring the real-world interconnectedness of chemical, biological, and toxicological concepts [63]. This model is uniquely suited for the systematic evidence mapping required in chemical risk assessment, as it naturally accommodates evolving evidence, integrates diverse data sources, and enables sophisticated, relationship-driven queries that reveal hidden patterns in the data [64].

Foundational Concepts: From Tables to Graphs

The Structural and Functional Divide

The core difference between relational databases and knowledge graphs is not merely technical but conceptual. Relational databases are built on the strict schema-first principle, where data must conform to predefined table structures and relationships (foreign keys) are implied rather than explicit [62]. In contrast, knowledge graphs employ a flexible, connection-first model, where relationships are stored as fundamental, tangible data elements with their own properties and types [64] [63]. This fundamental shift has direct implications for evidence mapping.

Table 1: Architectural and Performance Comparison: Relational Databases vs. Knowledge Graphs

Aspect	Relational Database (Flat Tables)	Knowledge Graph	Implication for SEMs
Data Model	Schema-first, rigid tables with rows/columns [62].	Flexible, graph-based with nodes, edges, and properties [63].	Accommodates new study types or endpoints without schema redesign.
Relationship Handling	Relationships via foreign keys; discovered at query time via JOINs [62].	Relationships are stored natively as first-class entities (edges) [62] [64].	Directly models "chemical A inhibits pathway B leading_to endpoint C".
Query Performance for Relationships	JOIN complexity grows exponentially with depth (O(log(n)) per JOIN) [62].	Constant-time traversal (O(1)) via index-free adjacency [62].	Enables real-time exploration of complex evidence chains.
Model Adaptability	Schema changes require significant restructuring and migration [62].	Dynamic schema; new node/edge types can be added seamlessly [62] [64].	Supports iterative development of evidence maps as new knowledge emerges.
Semantic Context	Low; meaning is inferred from schema and application logic.	High; explicit semantics via ontologies define meaning of relationships [64] [63].	Ensures consistent interpretation of evidence across research teams.

Quantifying the Performance Advantage for Evidence Traversal

The performance disparity is most critical when traversing multi-step relationships—a common task in identifying all studies related to a chemical's upstream metabolic pathway or downstream health effects. In a relational model, each hop in the chain requires a computational JOIN operation. As the graph depth increases, the computational cost grows polynomially, making real-time exploration of deep evidence chains impractical for large datasets [62].

Knowledge graphs leverage index-free adjacency, where each node stores direct pointers to its connected nodes. Traversing from one node to its neighbor becomes a simple pointer lookup in memory, resulting in constant-time complexity (O(1)) per hop [62]. This translates to performance gains of several orders of magnitude for relationship-heavy queries, allowing researchers to interactively explore connected evidence without pre-defined query paths [62].

Implementing Knowledge Graphs for Systematic Evidence Mapping

A Proven Methodology: From Unstructured Reports to an Analytical Knowledge Graph

The construction of a domain-specific knowledge graph for risk analysis follows a systematic, multi-stage pipeline. A seminal methodology for hazardous chemical accident (HCA) analysis demonstrates a transferable framework suitable for broader chemical risk assessment [65]. The process transforms unstructured or semi-structured text (e.g., accident reports, toxicological study abstracts) into a structured, queryable knowledge graph.

Table 2: Experimental Protocol for Knowledge Graph Construction from Unstructured Text [65]

Stage	Key Tasks	Tools & Techniques	Output & Purpose
1. Ontology Development	Define core entities, relationships, and attributes relevant to the domain.	Modified seven-step method for ontology engineering [65].	A formal, reusable schema (ontology) that standardizes concepts (e.g., Chemical, Study, Endpoint) and their relations (e.g., causes, measured_in).
2. Knowledge Extraction	Automatically identify entities and relationships from text corpora.	IRTI Model: A deep neural network for joint Relation-Triple Extraction, handling overlapping entities in long texts [65].	A set of structured triples (Subject, Predicate, Object) extracted from literature, forming the graph's raw material.
3. Knowledge Standardization & Enhancement	Normalize entity names and link to authoritative databases; infer implicit knowledge.	ChatGPT-4 & CLSTC Model: For entity normalization and clustering [65]. External regulatory databases for enrichment.	Cleaned, deduplicated entities linked to standard identifiers (e.g., CAS numbers), ready for graph population.
4. Graph Population & Storage	Load triples into a graph database and apply reasoning rules.	Graph databases (e.g., Neo4j, GraphDB) [64] [63]. Inference engines for deriving implicit facts.	The operational knowledge graph, enabling complex queries and pathway analysis.
5. Analysis & Visualization	Run graph algorithms and queries to uncover patterns.	Centrality measures, community detection, pathfinding algorithms [62] [65]. Interactive visualization tools.	Identification of key risk factors, common causal chains, and evidence clusters within the mapped literature.

Workflow Visualization: SEM-Driven Knowledge Graph Construction

The following diagram synthesizes the standard SEM methodology [3] [1] with the knowledge graph construction pipeline [65], illustrating the integrated workflow from problem formulation to analytical insight.

Diagram 1: Integrated SEM & Knowledge Graph Construction Workflow (Characters: 98)

Building and utilizing a knowledge graph for evidence mapping requires a combination of specialized software, databases, and analytical tools.

• Graph Database Management Systems: Software like Neo4j (property graph) or GraphDB/Amazon Neptune (RDF triplestore) provide the core engine for storing and querying interconnected data with high performance for traversal operations [64] [63].
• Ontology & Vocabulary Resources: Authoritative ontologies are crucial for semantic consistency. Key resources include the EPA's CompTox Chemicals Dashboard (for chemical identifiers and properties) [66], the Comparative Toxicogenomics Database (CTD) (for chemical-gene-disease relationships) [66], and biomedical ontologies like ChEBI (Chemical Entities of Biological Interest) and MONDO (disease ontology).
• Text Mining & NLP Tools: For automated knowledge extraction from literature. This includes pre-trained models for Named Entity Recognition (NER) and Relation Extraction, or custom model development using frameworks like spaCy or Transformers. The IRTI model [65] is a domain-specific example.
• Query & Visualization Interfaces: Cypher (for Neo4j) or SPARQL (for RDF) are used for querying. Tools like Neo4j Bloom [66] or Cytoscape enable intuitive graph exploration and visualization for researchers.
• FAIR Data Assessment Tools: Tools like the FAIR Checker and F-UJI automated evaluator [66] are essential for assessing the quality and interoperability of source data before integration, ensuring the resulting graph is built on reliable foundations.

Advanced Integration: Knowledge Graphs, LLMs, and Interactive SEMs

Powering Agentic AI and Natural Language Interfaces

A frontier in the field is the integration of knowledge graphs with Large Language Models (LLMs) to create intuitive, powerful interfaces for evidence exploration [64] [66]. LLMs alone can struggle with factual accuracy and reasoning over complex relationships. A knowledge graph acts as a structured, verifiable knowledge base that grounds the LLM's responses.

• Semantic Retrieval-Augmented Generation (RAG): When a user asks a natural language question (e.g., "What liver effects are linked to chronic exposure to Chemical X?"), the system first queries the knowledge graph to retrieve relevant facts, pathways, and connected studies. This structured context is then fed to the LLM to generate a coherent, evidence-based summary [64].
• Long-term Memory for Research Agents: Autonomous AI agents designed to monitor literature or update evidence maps can use the knowledge graph as a persistent memory, storing new findings and their connections to the existing evidence network [64].

System Architecture for an Interactive Evidence Platform

The following diagram illustrates the architecture of a modern platform that combines a FAIR chemical knowledge graph with LLMs to provide multiple access points for risk assessors, from chatbots to visual graph explorers [66].

Diagram 2: Integrated KG-LLM System for Chemical Evidence Access (Characters: 94)

Addressing Data FAIRness: A Prerequisite for Reliable Graphs

The utility of a chemical risk knowledge graph is dependent on the quality and interoperability of its source data. An assessment of ten major chemical data sources using FAIR principles reveals significant room for improvement [66]. Key findings include:

• Findability: While most sources have unique web identifiers, many lack machine-readable metadata and persistent identifiers (PIDs) for specific data points.
• Interoperability: A major challenge is the use of non-standard, publisher-specific formats and a lack of documented, computer-interpretable metadata schemas, hindering automated integration [66].
• Reusability: Licensing terms are often unclear, and detailed provenance information is frequently missing.

Table 3: FAIRness Assessment of Selected Chemical Data Sources (Representative Examples) [66]

Data Source	Key Strength	FAIRness Challenge	Impact on KG Integration
EPA CompTox Dashboard	Rich data aggregation, APIs.	Complex data model requires specialized mapping.	High value but requires careful ontology alignment.
ECHA REACH Factsheets	Authoritative regulatory data.	Data is primarily in human-readable HTML/PDF.	Requires extensive parsing and text extraction.
Comparative Toxicogenomics (CTD)	Curated chemical-gene-disease relationships.	High interoperability via standard vocabularies.	Ideal, structured source for biological pathway edges.
ChemSpider	Extensive chemical compound database.	Licensing and reuse conditions for bulk data can be ambiguous.	Potential restriction on downstream analytical use.

The transition from flat tables to flexible knowledge graphs is more than a technical optimization; it is a necessary evolution to manage the complexity of modern chemical risk assessment. By explicitly modeling the relationships between chemicals, biological pathways, study results, and health outcomes, knowledge graphs provide a dynamic, queryable representation of the evidence ecosystem. This structure directly addresses the core objectives of Systematic Evidence Maps, enabling the efficient identification of evidence clusters, causal chains, and critical knowledge gaps.

The integration of this technology with LLMs and user-friendly interfaces promises to democratize access to complex toxicological knowledge, allowing risk assessors and researchers to ask nuanced questions and receive answers grounded in a verifiable web of evidence [64] [66]. The path forward requires continued focus on data FAIRness at the source and the development of shared, community-approved ontologies for the environmental health domain. By doing so, the field can move from disconnected datasets to a truly connected, intelligent, and actionable knowledge infrastructure that accelerates the translation of science into protective decisions.

The field of chemical risk assessment is undergoing a fundamental transformation, driven by an explosion of available data and increasing demands for transparency and speed in regulatory decision-making. In this context, Systematic Evidence Maps (SEMs) have emerged as a critical, foundational tool. Unlike traditional systematic reviews, which answer a narrowly focused question with a resource-intensive synthesis, SEMs function as queryable databases of systematically gathered research [15]. They characterize broad features of the entire evidence base for a chemical or group of chemicals, enabling researchers and regulators to visually explore data trends, identify knowledge clusters, and pinpoint critical gaps [3]. This makes SEMs an indispensable precursor for prioritizing where to deploy deeper, more resource-intensive systematic reviews [15].

The construction and interrogation of these vast evidence maps, however, present significant challenges of scale and complexity. Manual processes are prohibitively slow and prone to inconsistency. This whitepaper argues that the integration of artificial intelligence (AI), automation, and specialized software is not merely an enhancement but a necessity for realizing the full potential of SEMs. These technologies streamline every phase of the evidence synthesis workflow—from literature search and screening to data extraction and dynamic visualization—thereby enhancing efficiency, reproducibility, and the ultimate utility of SEMs in supporting evidence-based chemical risk assessment and policy [67] [68].

Technical Foundations: From Rigid Tables to Dynamic Knowledge Graphs

The traditional approach to structuring data for evidence synthesis has relied on rigid, flat database tables or spreadsheets. While orderly, this schema-first, "on-write" model struggles with the highly connected and heterogeneous nature of toxicological data, where relationships between chemicals, outcomes, study models, and endpoints are complex and multidimensional [8].

An innovative solution to this limitation is the use of knowledge graphs. A knowledge graph is a flexible, schemaless data structure that stores information as a network of nodes (entities, such as a specific chemical or a health outcome) and edges (the relationships between them, such as "is associated with" or "was tested in") [8]. This model is inherently suited for environmental health data because it allows for the integration of diverse data types—from traditional mammalian bioassays and epidemiology to New Approach Methodologies (NAMs) like high-throughput screening data—without forcing them into a pre-defined, restrictive table [3] [8].

The following diagram contrasts the traditional linear data model with the interconnected knowledge graph model, illustrating the latter's superiority for managing complex evidence networks.

Traditional vs. Graph-Based Evidence Data Models

The shift from a linear to a graph-based model enables more powerful and intuitive querying. A regulator can now easily ask complex questions like, "Show me all studies on chemicals structurally similar to Chemical X that reported outcomes in the hepatic system," traversing the network of relationships rather than joining multiple disjointed tables [8].

AI and Automation in the Evidence Synthesis Workflow

The application of AI and automation transforms the SEM development pipeline from a manual, time-locked process into a dynamic and scalable operation. Key technologies are being deployed at specific stages to overcome major bottlenecks.

1. Intelligent Document Processing and Screening: The initial stages of systematic mapping involve screening thousands of bibliographic records and full-text articles against broad PECO (Populations, Exposures, Comparators, and Outcomes) criteria [3]. Machine learning classifiers, particularly those using active learning, can be trained to prioritize records likely to be relevant. The U.S. EPA, in partnership with AWS, has piloted the use of generative AI to automate the extraction of key data fields (e.g., study design, dose levels, outcomes) from PDFs of toxicological studies [67]. This moves beyond simple keyword matching to understanding semantic context within complex scientific text.

2. Predictive Toxicology and Read-Across: AI excels at finding patterns in high-dimensional data. In the context of SEMs, this capability is harnessed for predictive toxicology. Tools like the read-across tool RASAR (Read-Across Structure Activity Relationship) use machine learning to predict the toxicity of a data-poor chemical by leveraging data from structurally similar, data-rich chemicals [68]. An SEM enriched with such predictions can visually highlight data gaps while providing preliminary, computationally derived hazard indicators to guide testing priorities. Such models have demonstrated high accuracy, with RASAR achieving 87% balanced accuracy across numerous tests, rivaling or exceeding the reproducibility of some animal studies [68].

The following table summarizes the quantitative impact and applications of these technologies in the SEM workflow:

Table 1: Impact of AI & Automation on Systematic Evidence Mapping Workflows

Workflow Stage	Traditional Challenge	AI/Automation Solution	Reported Efficacy / Impact	Primary Benefit
Study Screening	Manual review of thousands of titles/abstracts is time-consuming and prone to reviewer fatigue.	Machine learning classifiers prioritize likely-relevant records for human review.	Reduces manual screening workload by 30-50% while maintaining sensitivity [67].	Accelerates the initial evidence inventory phase.
Data Extraction	Manual extraction from PDFs is error-prone and inconsistent across reviewers.	Generative AI & NLP models extract structured data (dose, outcome, species) from text [67].	Pilot projects demonstrate feasibility for automating key data fields in chemical assessments [67].	Dramatically increases throughput and ensures standardized data capture.
Evidence Prediction	Data gaps for many chemicals limit risk assessment.	Predictive models (e.g., RASAR) perform read-across from data-rich to data-poor chemicals [68].	Models achieving ~87% balanced accuracy, comparable to animal test reproducibility [68].	Populates evidence maps with predictive insights, guiding targeted testing.
Quality Evaluation	Assessing study reliability (risk of bias) requires expert judgment and is slow.	AI models trained on expert evaluations can provide consistent preliminary risk-of-bias flags.	Under active research; potential to standardize and expedite critical appraisal [67].	Increases consistency and frees expert time for complex edge-case evaluations.

Protocol for Dynamic Visualization and Interactive Interrogation

A static PDF report is an insufficient endpoint for a rich SEM. The true value is unlocked through interactive visualization that allows end-users—risk assessors, project managers, and policy analysts—to dynamically explore the evidence based on their specific questions [69].

Experimental Protocol: Creating an Interactive SEM Dashboard

This protocol details the process for transforming extracted SEM data into an interactive analytical tool, based on proven methodologies [69].

• Objective: To develop a web-based, interactive dashboard that allows users to filter, visualize, and explore the study and outcome data contained within a systematic evidence map for a group of chemicals.

• Materials & Input Data: The prerequisite is a structured dataset extracted during the SEM process. Following the EPA SEM template [3], this typically includes:

  • Study Metadata Table: Study ID, citation, chemical tested, test system (species, in vitro model), exposure duration, study type (bioassay, epidemiology).
  • Outcome Data Table: Outcome ID, linked Study ID, specific endpoint measured (e.g., "liver weight," "serum ALT"), direction of effect, reported quantitative result (e.g., mean, SD).
  • Chemical Descriptor Table: Chemical ID, name, CASRN, structural properties.

• Procedure:

  • Data Model Preparation: Structure the extracted data into a relational format suitable for visualization software (e.g., an Excel workbook with linked tables or a SQL database). Each row should represent a single observation (e.g., one outcome from one study) [69].
  • Tool Selection & Import: Select a business intelligence or visualization platform (e.g., Tableau, Power BI, R Shiny). Import the linked data tables and establish relationships between them (e.g., linking Outcome Data to Study Metadata via Study_ID).
  • Visualization Design:
    • Create a filter panel with drop-down menus for key variables: Chemical, Test System, Health Outcome System (e.g., hepatic, neurological), Study Type.
    • Build a main visual summary, such as a bubble chart or heatmap, where the x-axis represents chemicals, the y-axis represents outcome systems, and the bubble size/color represents the volume or strength of evidence.
    • Develop a detailed view panel, such as a dynamic forest plot or a data table, that lists individual studies and their extracted results. This panel should be linked to the main summary so that clicking on a bubble filters the detailed list to the relevant studies.
    • Implement tooltips that display key study details (dose, species, effect size) when a user hovers over a data point [69].
  • Dashboard Assembly & Deployment: Assemble the visualizations into a single dashboard interface. Configure it so that actions in the filter panel update all visualizations simultaneously. Publish the dashboard to a web server or secure cloud platform for stakeholder access.

• Output: An interactive, web-accessible dashboard. A user can, for example, select "Hepatic System" and "Bisphenol A" to see all associated liver outcomes, click on a cluster for "liver weight" to view the five rodent studies contributing to that signal, and then export the list of those studies for further analysis.

This dynamic capability moves evidence delivery from a static answer to a specific question towards an explorable resource that supports iterative inquiry and problem formulation [69].

The Scientist's Toolkit: Essential Solutions for Automated Evidence Synthesis

Building and leveraging AI-enhanced SEMs requires a suite of interoperable software and reagent solutions. The following toolkit categorizes essential resources for modern research teams.

Table 2: Research Reagent & Software Solutions for Automated Evidence Synthesis

Tool Category	Example Solutions	Primary Function in SEM Workflow	Key Consideration for Selection
Literature Management & Screening	DistillerSR, Rayyan, Covidence	Manages the import of search results, facilitates dual-independent screening (title/abstract, full-text), and tracks exclusions with reasons.	Integration with bibliographic databases (PubMed, SCOPUS), support for machine learning prioritization features, and audit trail completeness.
AI-Powered Data Extraction	Custom NLP pipelines (e.g., spaCy, BERT), Amazon Textract/Bedrock [67], SciBite	Automates the extraction of structured data (PECO elements, numerical results) from PDFs of scientific literature.	Accuracy on domain-specific toxicology text, ability to handle tables and figures, and configurability for custom data fields.
Data Structuring & Storage	SQL databases (PostgreSQL), NoSQL graphs (Neo4j), Spreadsheets (Excel)	Provides the backbone for storing and organizing extracted, structured data. Choice depends on data complexity.	For complex, relational data, graph databases (Neo4j) are superior for capturing interconnected evidence [8]. For simpler maps, SQL or spreadsheets may suffice.
Predictive Modeling	RASAR tools [68], OECD QSAR Toolbox, EPA CompTox Chemicals Dashboard	Applies machine learning and read-across to predict hazard properties for chemicals lacking experimental data, enriching the evidence map.	Transparency of the model (explainable AI/xAI), regulatory acceptance, and applicability domain for the chemicals of interest [68].
Interactive Visualization	Tableau [69], Power BI, R Shiny, Python (Plotly Dash)	Transforms structured evidence data into interactive dashboards, heatmaps, and forest plots for exploration and communication.	Ease of use for developers and end-users, web deployment capabilities, and ability to handle the project's data volume and update frequency.
Color Contrast & Accessibility	WebAIM Contrast Checker, Adobe Color Contrast Analyzer, NoCoffee Vision Simulator	Ensures that all data visualizations and user interfaces meet WCAG guidelines (minimum 4.5:1 for text) [70] [71], making the SEM accessible to all users.	Must be used during design and testing phases to avoid creating visual barriers to information, which is critical for public and regulatory tools [72].

The integration of these tools into a coherent pipeline is the final step. The following diagram illustrates how these components interact in an optimized, semi-automated workflow for constructing and deploying a SEM.

Integrated AI-Enhanced Workflow for Systematic Evidence Mapping

The systematic mapping of chemical evidence is evolving from a manual, academic exercise into a dynamic, technology-driven pillar of modern risk assessment. By strategically integrating AI for data extraction and prediction, automation for workflow efficiency, and interactive software for data exploration, researchers can construct living, queryable evidence maps that are far more comprehensive, accessible, and actionable than traditional reviews.

This technological integration directly addresses the core challenges in chemical risk assessment: managing volume, mitigating bias, and providing timely, relevant evidence for decision-making. As outlined by the U.S. EPA's own pioneering work [67] [3] and academic research [68] [8], the future of evidence synthesis is not just faster literature reviews, but a fundamentally more powerful evidence surveillance and interrogation system. For scientists and drug development professionals, adopting this integrated toolkit is essential for staying at the forefront of rigorous, transparent, and impactful chemical safety evaluation.

In the field of chemical risk assessment, researchers and regulators are confronted with a vast, fragmented, and rapidly expanding evidence base. Systematic Evidence Maps (SEMs) have emerged as a critical tool to navigate this complexity [1]. An SEM is a form of evidence synthesis that provides a structured, visual overview of the available research landscape [1]. Its primary function is to categorize and organize scientific evidence, thereby identifying dominant research trends, substantive knowledge clusters, and, crucially, significant evidence gaps [1]. This process lays an essential foundation for prioritization, informing decisions on where to commission new primary research or conduct more resource-intensive systematic reviews [1].

For drug development professionals and toxicological researchers, the value of an SEM is twofold. First, it transforms a disparate collection of studies into a navigable map, offering clarity on what is known about a chemical's effects across different health systems, exposure levels, and model organisms. Second, and central to this guide, a rigorously conducted SEM ensures transparency and reproducibility. By adhering to standardized reporting standards and explicit protocols, an SEM mitigates the risk of bias, allows for independent verification, and enables the seamless integration or updating of evidence as new studies emerge [73]. This technical guide details the methodologies and standards necessary to achieve this rigor within the context of chemical risk assessment.

Foundational Methodology of a Systematic Evidence Map

The construction of a reliable SEM follows a defined, stepwise workflow designed to minimize arbitrariness and error. The following diagram illustrates this core methodological framework.

Figure 1: The Six-Step Systematic Evidence Map Workflow [1] [3].

Defining the Scope and Protocol

The process is initiated by developing a detailed, publicly accessible protocol. This pre-registered plan defines the SEM's objectives, scope, and all methodological steps, guarding against arbitrary decision-making during the review [73]. A cornerstone of this stage is formulating the review question using a structured framework. In environmental health and toxicology, the PECO framework (Population, Exposure, Comparator, Outcome) is standard [3] [73]. For a chemical risk assessment SEM, this translates to:

• Population: Human populations (for epidemiological studies) or specific mammalian/non-mammalian model organisms (e.g., rats, zebrafish).
• Exposure: The specific chemical or chemical class of interest, including relevant exposure routes (oral, inhalation, dermal).
• Comparator: Unexposed, vehicle-control, or lower-dose groups.
• Outcome: Any measured health effect or endpoint (e.g., hepatotoxicity, neurodevelopmental effects, carcinogenicity).

Keeping the PECO criteria broad at this stage ensures a comprehensive capture of the evidence landscape [3]. The protocol must also specify plans for handling supplemental evidence, such as in vitro studies, pharmacokinetic data, or New Approach Methodologies (NAMs), which are tracked separately from the main PECO-relevant studies [3].

Systematic Search Strategy

A transparent and reproducible search strategy is the engine of the SEM. The goal is to collate a maximum number of relevant articles while minimizing search bias [73]. This involves searching multiple bibliographic databases (e.g., PubMed, Embase, Scopus, TOXLINE) and complementary sources like regulatory dossiers and grey literature [73]. The search strategy is built from search strings that combine terms for each PECO element using Boolean operators (AND, OR) [73]. A critical step is peer-reviewing the search strategy, often with a librarian, to identify missing terms or syntax errors [73]. Key biases to mitigate include:

• Publication Bias: Over-reliance on published, statistically significant results. Mitigated by proactively searching grey literature and trial registries [73].
• Language Bias: Searching only English-language sources. Mitigated by including major non-English databases where feasible [73].
• Database Bias: Using only one or two databases. Mitigated by a multi-database, multi-source approach [73].

Screening and Selection

Identified records are screened against the eligibility criteria in a two-phase process, typically performed by two independent reviewers to minimize error [3] [73]. The first phase screens titles and abstracts, while the second involves a full-text review of potentially relevant articles. Specialized systematic review software (e.g., Rayyan, Covidence, DistillerSR) is used to manage this process, track decisions, and resolve conflicts between reviewers. This stage outputs the final corpus of studies for data extraction.

Data Extraction and Coding

For each included study, data is extracted into a standardized, pre-piloted form [3]. Extraction is usually performed by a single reviewer with verification by a second [3]. The goal is not to extract every quantitative result (as in a meta-analysis) but to capture key descriptive and methodological metadata that enables categorization and mapping. The US EPA SEM template tracks data such as [3]:

• Study design (e.g., cohort, case-control, randomized trial, bioassay).
• Test system (species, strain, cell line).
• Exposure details (dose, duration, route).
• Health systems/outcomes assessed (e.g., hepatic, renal, neurological).
• Study duration and year of publication.

Data Synthesis and Visualization

Synthesis in an SEM is primarily narrative and descriptive, focusing on patterns in the extracted metadata [1]. The coded data is visualized using interactive heatmaps, bubble plots, and evidence atlases to show the volume and distribution of research across chemicals, outcomes, and study types [1]. These visual tools make evidence gaps and clusters immediately apparent to stakeholders and decision-makers.

Reporting and Outputs

The final SEM report must document every step with sufficient detail to allow replication. Interactive outputs, often hosted on dedicated websites, allow users to filter and explore the mapped evidence dynamically [1] [3].

Detailed Protocols for Key Experimental and Review Phases

Protocol for Systematic Searching and Screening

The following diagram details the specific, replicable steps for the search and screening phase, a critical juncture for ensuring transparency.

Figure 2: Detailed Protocol for Systematic Search and Screening [3] [73].

The quantitative outcomes of this phase are systematically recorded. The following table summarizes the key metrics and their importance for reporting.

Table 1: Key Quantitative Metrics for Search and Screening Reporting

Metric	Description	Purpose in Reporting
Total Records Identified	Sum of records from all databases and sources before deduplication.	Demonstrates the breadth of the initial search.
Records After Deduplication	Number of unique records remaining.	Provides the actual screening workload.
Records Screened (Title/Abstract)	Number of records assessed in the first screening phase.	Base for calculating exclusion rates.
Full-Text Articles Assessed	Number of reports retrieved and screened for eligibility.	Indicates the depth of the review process.
Studies Included in SEM	Final number of studies meeting all PECO criteria.	The core output, defining the mapped evidence base.
Inter-Reviewer Reliability (Kappa)	Statistical measure of agreement between independent screeners.	Quantifies the consistency and objectivity of the screening process.

Protocol for Data Extraction and Synthesis

The data extraction phase translates study details into codable, analyzable metadata. A rigorous protocol ensures consistency and accuracy.

Table 2: Standardized Data Extraction Fields for a Chemical Risk SEM

Extraction Field Category	Specific Data Points	Coding Example
Study Identification	Author, Year, DOI, Study Type (e.g., rodent bioassay, cohort).	Smith et al., 2023; 10.1016/j.tox.2023.123456; Chronic Toxicity Study.
Test System	Species, Strain, Sex, Age/Life Stage, Sample Size.	Rat; Sprague-Dawley; Male & Female; Adult; n=50/group.
Exposure Regimen	Chemical Name (CAS RN), Dose/Concentration, Route, Duration.	Chemical X (123-45-6); 0, 10, 50, 200 mg/kg/day; Oral gavage; 90 days.
Outcomes Assessed	Health System, Specific Endpoint, Measurement Method.	Hepatic; Serum ALT; Clinical chemistry analyzer.
Results Direction	Effect Direction (Increase, Decrease, No Effect), Statistical Significance (p-value).	Increase; p < 0.01.
Risk of Bias Indicators	Randomization, Blinding, Compliance with OECD/EPA guidelines.	Yes; No; Fully compliant.

The synthesis protocol involves organizing this extracted data into a structured database. The following diagram outlines the workflow from extracted data to synthesis and visualization.

Figure 3: Data Extraction to Synthesis Workflow [1] [3].

Standards for Data Presentation and Visualization

Principles of Effective Tabular and Visual Presentation

Clear data presentation is not ancillary; it is fundamental to the utility and transparency of an SEM. Non-textual elements (tables, figures) should be used strategically to summarize complex information, break textual monotony, and promote deeper understanding [74]. A general guideline is to include approximately one non-textual element per 1,000 words of manuscript [74]. Each element must be self-explanatory, with a clear title, legend, and footnotes defining abbreviations and notes [74].

The choice between a table and a figure depends on the message:

• Use Tables to present precise numerical values or detailed descriptive information where the reader needs to reference specific data points [74]. They are ideal for summarizing study characteristics or extracted data.
• Use Figures (Graphs, Charts, Maps) to illustrate trends, patterns, relationships, or the overall distribution of evidence [74]. They are more effective for showing gaps and clusters at a glance.

Table 3: Guidelines for Selecting and Designing Visual Elements

Element Type	Best Use Case	Key Design Principle	Common Pitfall to Avoid
Table	Presenting exact values; summarizing study metadata; listing inclusion criteria.	Order rows meaningfully; use consistent formatting; limit to essential columns [74].	Creating crowded, overly complex tables that are difficult to scan [74].
Heatmap	Showing the volume/density of evidence across two categorical dimensions (e.g., Chemical vs. Outcome).	Use an intuitive, sequential color scale (e.g., light to dark).	Using a non-sequential or misleading color palette.
Bar Graph	Comparing quantities across discrete categories (e.g., number of studies per health system).	Always start the numerical axis at zero to accurately represent magnitude [74].	Using distorted scales that exaggerate differences.
Symbol Map (Evidence Atlas)	Displaying the geographical distribution of research or study locations [75].	Ensure symbols do not overlap excessively and are sized proportionally to the data value [75].	Overloading the map with multiple, conflicting visual variables (size, color, shape) [75].

Adherence to Visual Accessibility and Contrast Standards

To ensure findings are accessible to all users, including those with visual impairments, visual elements must comply with the Web Content Accessibility Guidelines (WCAG). For graphical objects within charts and diagrams—such as bars, plot points, and legend icons—a minimum contrast ratio of 3:1 against adjacent colors is required (WCAG Success Criterion 1.4.11) [28] [71]. This is distinct from text contrast requirements and is critical for distinguishing elements in a graph.

When creating diagrams, the following rules must be applied to the specified color palette (#4285F4, #EA4335, #FBBC05, #34A853, #FFFFFF, #F1F3F4, #202124, #5F6368):

• Text Contrast Rule: For any shape (node) containing text, the fontcolor must be explicitly set to ensure high contrast against the node's fillcolor.
• Arrow/Symbol Contrast: The color of arrows, lines, and symbols must have sufficient contrast against the background color (bgcolor) of the diagram.
• Palette Application: The provided palette includes colors that, when paired correctly, meet these standards. For example, dark text (#202124) on a light background (#FFFFFF, #F1F3F4, #FBBC05) provides excellent contrast, while white text on the vibrant blues, greens, and reds also meets requirements.

The Scientist's Toolkit: Essential Research Reagent Solutions

The following table details key materials, software tools, and resources essential for conducting a transparent and reproducible SEM in chemical risk assessment.

Table 4: Essential Toolkit for Systematic Evidence Mapping

Tool/Resource Category	Specific Item	Function & Purpose
Protocol & Reporting Standards	PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews)	Provides a checklist for transparent reporting of the SEM methods and findings.
	CADIMA (CEEDER) or PROSPERO Platform	Open-access software platform to plan, conduct, and document the SEM process; some allow protocol registration.
Search & Screening Software	Bibliographic Databases (PubMed, Embase, Scopus, TOXLINE)	Primary sources for identifying peer-reviewed scientific literature [73].
	Systematic Review Software (Rayyan, Covidence, DistillerSR)	Platforms for collaborative title/abstract screening, full-text review, and conflict resolution [3].
Data Extraction & Management	Customized Data Extraction Forms (e.g., via Google Sheets, Airtable, DistillerSR)	Standardized, piloted digital forms for accurate and consistent capture of study metadata [3].
	Reference Management Software (EndNote, Zotero, Mendeley)	Manages citations, removes duplicates, and stores PDFs.
Synthesis & Visualization Tools	Data Visualization Software (Tableau, R ggplot2, Python Matplotlib/Seaborn)	Creates interactive heatmaps, bubble plots, and other visualizations to represent the evidence map [1].
	Qualitative Synthesis Tools (NVivo, Dedoose)	Can assist in coding and analyzing themes in large textual data from studies.
Critical Appraisal Tools	Risk of Bias (RoB) Tools (e.g., OHAT RoB Tool, SYRCLE's RoB for animal studies)	Structured guides to assess the methodological quality and internal validity of included studies, when appraisal is conducted [1].

The adoption of Systematic Evidence Maps represents a paradigm shift towards greater transparency and strategic oversight in chemical risk assessment research. To fully realize their potential, the following actions are recommended for researchers, institutions, and regulators:

• Mandate Protocol Pre-registration: Funding bodies and journals should require the public registration of SEM protocols prior to initiation. This commits to a plan, reduces selective reporting, and allows for peer feedback on methodology.
• Invest in Training and Specialist Support: Conducting a high-quality SEM requires specific skills in information science, data management, and systematic review methodology. Access to research librarians and methodological specialists should be considered integral to the process [73].
• Develop and Adopt Standardized Templates: Widespread adoption of standardized templates—like the one provided by the US EPA [3]—for data extraction and reporting will enhance consistency and comparability across different SEMs.
• Prioritize Interactive and Living Outputs: SEMs should not be static PDF reports. The evidence base is dynamic. Hosting SEMs as interactive, updatable web platforms ensures they remain useful tools for decision-makers over time [1].
• Integrate with New Approach Methodologies (NAMs): SEM protocols must explicitly plan for the categorization and tracking of evidence from high-throughput in vitro assays, in silico models, and other NAMs. Mapping this evolving evidence stream is critical for modern, integrated risk assessment frameworks [3].

By rigorously adhering to the reporting standards and detailed protocols outlined in this guide, the scientific community can produce SEMs that are not only scientifically robust but also powerful, transparent instruments for guiding research investment and informing evidence-based policy in chemical risk assessment.

The paradigm shift in regulatory toxicology from traditional animal-based testing to New Approach Methodologies (NAMs) is generating unprecedented volumes of complex, heterogeneous data [76]. This revolution, while promising higher-throughput and more mechanistic understanding of chemical hazards, presents a significant integration challenge for chemical risk assessment [76]. Within this context, Systematic Evidence Maps (SEMs) have emerged as a critical tool for navigating and synthesizing broad evidence bases, serving as problem formulation tools and assisting in priority setting [3].

This technical guide outlines robust strategies for coding, managing, and visualizing heterogeneous toxicological evidence—from high-throughput in vitro assays and transcriptomic data to traditional in vivo studies and epidemiological evidence—within the framework of developing systematic evidence maps. The goal is to facilitate the effective use of NAMs by creating transparent, queryable, and actionable evidence structures that support Next Generation Risk Assessment (NGRA) [76] [77].

Foundational Data Integration Strategy

A successful integration strategy hinges on a system-thinking approach that considers not just technical data types but also the social and procedural components of the regulatory system [76]. Data coding must facilitate the transition from isolated data points to actionable evidence for decision-making.

Core Data Streams and Coding Objectives: The primary challenge is harmonizing data from divergent evidence streams. The following table summarizes key data types and the coding strategies required to integrate them into a cohesive SEM.

Table 1: Heterogeneous Data Streams and Integration Strategies for SEMs

Evidence Stream	Primary Data Types	Key Coding Challenges	Proposed Coding Strategy
Traditional In Vivo	Mammalian bioassay data, histopathology, clinical observations [3].	Standardizing effect severity, extracting dose-response data, reconciling varied study designs.	Use of structured PECO (Population, Exposure, Comparator, Outcome) frameworks for extraction [3] [2]. Coding for species, strain, dose, and adverse outcome.
Epidemiological	Human cohort/ case-control data, exposure biomarkers, health outcome data [3] [13].	Handling confounding variables, diverse exposure metrics, and varied statistical reporting.	Coding for study design, population characteristics, exposure assessment method, effect size, and confidence intervals.
New Approach Methodologies (NAMs)	High-throughput screening (HTS), transcriptomics, in silico predictions, high-content imaging [3] [77].	Defining bioactivity thresholds, linking in vitro targets to adverse outcomes, processing high-dimensional data.	Coding for assay endpoint, target, potency (e.g., AC50), efficacy, and use of in vitro-to-in vivo extrapolation (IVIVE) to derive oral equivalent doses (OEDs) [77].
Toxicokinetic	ADME (Absorption, Distribution, Metabolism, Excretion) data, PBPK models [3].	Integrating parameters for IVIVE, reconciling differences across systems.	Coding for key parameters (e.g., clearance, fraction unbound) and model type to support quantitative extrapolation.

A pivotal application of coded data is quantitative hazard banding, which transforms diverse toxicity values into categorical hazard levels. Recent methodologies leverage expanded datasets to increase confidence. For example, a 2025 framework created hazard bands by categorizing probabilistic reference doses (pRfDs) and endocrine-related qHTS data into quintiles [77].

Table 2: Example Quantitative Hazard Banding Using pRfD Data [77]

Hazard Band	pRfD Range (mg/kg-day)	Interpretation (Severity)	Typical GHS Hazard Statement Association
HB1	>10	Very Low	May be harmful if swallowed (H302)
HB2	1 - 10	Low	Harmful if swallowed (H302)
HB3	0.1 - 1	Medium	Toxic if swallowed (H301)
HB4	0.01 - 0.1	High	Fatal if swallowed (H300)
HB5	<0.01	Very High	Fatal if swallowed (H300)

Experimental Protocols for Evidence Coding

The creation of a reliable SEM requires a rigorous, pre-specified protocol to ensure transparency, reproducibility, and minimize bias [3] [2] [13]. The following workflow is adapted from established EPA and research protocols.

Protocol: Systematic Evidence Map Development for Heterogeneous Toxicological Data

1. Protocol Registration & Scope Definition:

• Objective: Define the SEM's scope using a PECO or PCC (Population, Concept, Context) statement [2]. For example: "To map evidence on chemical exposures (E) and adverse hepatic outcomes (O) in mammalian systems (P)."
• Inclusion/Exclusion: Establish criteria for evidence streams. Typically, mammalian bioassays and epidemiology are core, while NAMs (in vitro, in silico) and toxicokinetic data are tracked as supplemental evidence [3].
• Pre-publish the protocol to reduce expectation bias [2].

2. Comprehensive Search & Deduplication:

• Sources: Search multiple databases (e.g., PubMed, Web of Science, Scopus) without date/language filters [13].
• Strategy: Use controlled vocabulary (MeSH) and text words for exposure, outcomes, and study types.
• Tools: Employ systematic review software (e.g., DistillerSR, Rayyan) for deduplication and initial screening [13].

3. Screening & Eligibility:

• Process: Conduct screening in two phases (title/abstract, then full-text) with two independent reviewers [3]. Machine learning tools can prioritize records.
• Eligibility: Apply pre-defined criteria to categorize studies into core (PECO-relevant) and supplemental evidence buckets [3].

4. Data Extraction & Coding:

• Tool: Use standardized, web-based extraction forms or semi-automated software (e.g., DEXTR) [13].
• Variables: Extract and code:
  • Study Identifiers & Design: Citation, study type (in vivo, epidemiological, in vitro HTS, etc.).
  • Exposure: Chemical, dose/concentration, duration, route.
  • System: Species, cell line, model type.
  • Outcome: Endpoint measured (e.g., clinical observation, transcriptomic signature, cytotoxicity AC50).
  • Results: Quantitative findings (e.g., NOAEL/LOAEL, benchmark dose, hit-call, effect size).
  • Reporting Quality: Key internal validity items (blinding, randomization, concentration verification).

5. Study Evaluation & Data Curation:

• Evaluation: Conduct risk-of-bias or reliability assessment on a case-by-case basis depending on the SEM's purpose [3].
• Curation: Transform extracted data into a standardized, analyzable format. This includes unit conversion, deriving consensus values from multiple studies, and applying bioactivity thresholds to HTS data [77].

6. Visualization & Database Creation:

• Product: Develop an interactive, queryable database.
• Tools: Implement using visualization software (e.g., Tableau) or custom dashboards to create the final evidence map [78] [13].
• Output: Generate visualizations such as heatmaps (e.g., chemicals vs. outcomes) and evidence atlases.

Visualizing Relationships: From Data to Decisions

Effective visualization is critical for interpreting complex evidence relationships. Diagrams must adhere to accessibility standards, ensuring a minimum contrast ratio of 4.5:1 for standard text and 7:1 for smaller text against background colors [27]. The following diagram illustrates the logical relationship between heterogeneous evidence streams and risk assessment conclusions within an SEM framework.

Implementing the strategies above requires a suite of specialized tools. This toolkit extends beyond laboratory reagents to encompass software and frameworks essential for evidence coding and integration.

Table 3: Research Reagent Solutions for Evidence Coding and Integration

Tool Category	Specific Tool / Resource	Primary Function in Evidence Coding	Key Consideration
Systematic Review Software	DistillerSR [13], Rayyan, CADIMA	Manages the SEM workflow: deduplication, screening, extraction. Ensures audit trail and reviewer coordination.	Cloud-based platforms facilitate remote team collaboration and maintain protocol adherence.
Data Extraction & Curation	DEXTR (semi-automated extraction) [13], Custom web forms, SQL/Python scripts	Standardizes data pull from PDFs or databases into structured fields (e.g., chemical ID, dose, outcome).	Balance between automation (speed) and manual review (accuracy). Define quality control checks.
Bioactivity Analysis	R/Bioconductor packages (e.g., tcpl), Commercially available HTS analysis suites	Processes raw HTS/transcriptomic data, calculates potency (AC50), applies hit-calling algorithms.	Standardization of processing pipelines is critical for reproducibility and cross-study comparison.
Toxicokinetic IVIVE	High-throughput toxicokinetic models (e.g., HTTK R package), Berkeley Madonna (for PBPK)	Converts in vitro concentration-response to in vivo oral equivalent doses (OEDs) for hazard banding [77].	Model selection and parameterization must be transparent and fit-for-purpose.
Visualization & Dashboarding	Tableau [13], R (ggplot2, urbnthemes) [25], Spotfire, Custom dashboards [78]	Creates interactive evidence maps, heatmaps, and chemical lifecycle dashboards for stakeholder exploration.	Follow visualization best practices: use sequential, categorical, or diverging color palettes appropriately [79]. Ensure color contrast and accessibility [27] [80].
Evidence Integration Framework	WoE (Weight of Evidence) frameworks, AOP (Adverse Outcome Pathway) knowledgebase, ITS (Integrated Testing Strategy)	Provides a logical structure for integrating and interpreting data across evidence streams to support conclusions.	Frameworks must be pre-defined in the protocol to minimize bias during integration.

Systematic Evidence Maps (SEMs) represent a transformative methodological framework within evidence synthesis, designed to systematically categorize and organize vast scientific evidence landscapes to identify research trends and critical knowledge gaps [1]. In the context of chemical risk assessment—a field burdened by legacy chemicals, an influx of new substances, and increasingly complex, multi-disciplinary data—SEMs offer a pragmatic solution for transparent and resource-efficient evidence management [2]. This technical guide details the architectural and methodological principles required to future-proof SEMs, focusing on scalable data infrastructure and automated, continuous evidence surveillance. By integrating scalable cloud architectures, machine learning-aided workflows, and living update protocols, SEMs can evolve from static reviews into dynamic, decision-support tools. This evolution enhances the agility of regulatory frameworks like REACH and TSCA, supports targeted systematic reviews, and ultimately strengthens the foundation for evidence-based chemical risk management [2].

The chemical risk assessment landscape is defined by a fundamental tension: the need for meticulous, conclusive evidence syntheses versus the practical constraints of time, resources, and exponentially growing data. Traditional Systematic Reviews (SRs), while robust, are often ill-suited for rapid, exploratory, or broad-scope questions due to their intensive resource requirements and narrow PECO (Population, Exposure, Comparator, Outcome) focus [2]. Regulatory bodies face an overwhelming influx of data from diverse sources, including traditional in vivo studies, high-throughput in vitro assays, and computational toxicology models [2].

Systematic Evidence Maps (SEMs) address this gap by providing a comprehensive, queryable overview of an evidence base. They systematically catalog available research, characterizing key features such as studied chemicals, health outcomes, study designs, and model systems, without performing a full synthesis or meta-analysis [1] [2]. The core value proposition of an SEM is its ability to inform strategic decisions: prioritizing chemicals for full risk assessment, identifying clusters of evidence suitable for a subsequent SR, or highlighting critical data gaps needing primary research [2].

However, to fulfill this role sustainably, SEMs themselves must be designed for longevity and adaptability. "Future-proofing" in this context entails building systems that are: 1) Scalable, capable of managing exponentially increasing data volumes and complexity; 2) Adaptable, able to incorporate new data types (e.g., genomics, real-world data) and evolving scientific questions; and 3) Sustainable, supporting continuous, automated evidence surveillance rather than costly, one-off projects [81]. This guide outlines the technical and methodological framework for achieving these objectives.

Foundational Principles of Systematic Evidence Mapping

An SEM is a database of systematically gathered research, characterized by a predefined, transparent methodology [1]. Its primary output is not a pooled effect estimate, but a structured map of the evidence landscape, often visualized through interactive heatmaps, network diagrams, or evidence atlases [1].

Core Workflow Stages: The standardized workflow for an SEM involves several key stages [1]:

• Scope Definition: Establishing the review question, boundaries, and inclusion criteria.
• Systematic Search: Executing a comprehensive, reproducible search across multiple bibliographic databases and grey literature sources.
• Screening: Applying inclusion/exclusion criteria, typically in a title/abstract and full-text two-phase process.
• Data Coding & Extraction: Capturing predefined descriptive data from each study (e.g., chemical, outcome, study type).
• Critical Appraisal (Optional): Assessing study reliability or risk of bias, often included when evidence will inform a subsequent synthesis [1].
• Visualization & Reporting: Generating static and interactive visualizations and a final report summarizing the evidence landscape.

Table 1: Comparative Analysis: Systematic Review (SR) vs. Systematic Evidence Map (SEM)

Feature	Systematic Review (SR)	Systematic Evidence Map (SEM)
Primary Objective	To synthesize evidence to answer a specific, narrow question (e.g., effect estimate).	To catalog and characterize the broad evidence base to identify trends, clusters, and gaps [2].
Research Question	Tightly focused, typically via PECO statement.	Broad and exploratory, scoping the available research on a topic [2].
Data Synthesis	Mandatory qualitative and/or quantitative (meta-analysis) synthesis.	No synthesis; focuses on descriptive categorization of evidence [1].
Critical Appraisal	Mandatory risk of bias/quality assessment for included studies.	Optional; may be included to characterize the reliability of the evidence base [1].
Output	Pooled effect estimate, statement of confidence (e.g., GRADE).	Searchable database, visual maps (heatmaps, networks), report on evidence volume and distribution [1].
Time & Resource Intensity	Very high (12-24+ months).	Moderate to high, but typically less than a full SR due to less granular data extraction [2].
Ideal Use Case	Regulatory decision on a specific chemical-outcome linkage.	Priority-setting, informing the need for an SR, guiding a research agenda [2].

Thesis Context: Within chemical risk assessment research, SEMs serve as a critical upstream tool. They enable regulators and scientists to navigate the "data deluge" by providing an evidence-based rationale for where to allocate scarce resources for deeper analysis (via SR) or new testing [2]. This is especially pertinent for programs evaluating large numbers of chemicals, such as the US EPA’s Toxic Substances Control Act (TSCA) or the EU’s Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) [2].

Architectural Design for Scalable SEM Systems

Future-proofing requires an infrastructure that can grow seamlessly with data volume and user demand. A monolithic, static database is inadequate. Instead, a modular, cloud-native architecture is essential.

Core Architectural Components:

• Modular Data Pipeline: The evidence processing workflow should be decomposed into independent, containerized modules (e.g., search aggregation, deduplication, machine learning screening, data extraction). This allows individual components (like a new ML model) to be updated or scaled without overhauling the entire system.
• Cloud-Native Storage & Compute: Leveraging cloud object storage (e.g., AWS S3, Google Cloud Storage) for raw and processed data ensures unlimited scalability. Serverless computing functions or managed Kubernetes clusters can handle variable computational loads during large-scale screening or analysis bursts [81].
• API-First Design: All core functionalities—data submission, querying, visualization—should be accessible via well-documented Application Programming Interfaces (APIs). This enables interoperability, allowing the SEM database to connect with other regulatory tools (e.g., chemical inventory databases, adverse event reporting systems).
• Elastic Search & Query Layer: Implementing a dedicated search engine (e.g., Elasticsearch) enables complex, high-speed queries across millions of study records, supporting user-friendly exploration of the evidence map.

Table 2: Technical Specifications for a Scalable SEM Infrastructure

Layer	Technology Options	Function & Scalability Benefit
Ingestion & Processing	Apache Kafka, AWS Kinesis; Docker/Kubernetes	Handles high-velocity streams of new literature; containerization allows isolated scaling of each pipeline stage [81].
Storage (Raw/Processed)	Cloud Object Storage (S3, Blob), Graph Databases (Neo4j)	Cost-effective, durable storage for any data volume; graph databases efficiently model complex chemical-evidence-outcome relationships [81].
Indexing & Search	Elasticsearch, OpenSearch	Enables near real-time, complex full-text and field-specific searches across the entire evidence base.
Computation & Analytics	Serverless Functions (AWS Lambda), Managed Spark (Databricks)	Executes on-demand data processing and machine learning tasks without managing servers; scales automatically with job size [81].
API & Integration	RESTful API (FastAPI, Spring), GraphQL	Provides standardized, secure access for both human users and other software systems to query and retrieve data.

Scalable SEM system architecture for evidence processing

Security and Resilience: A scalable architecture must also be secure and resilient. This involves implementing zero-trust security principles, encrypting data at rest and in transit, and designing for high availability and disaster recovery to maintain operational continuity [81].

Implementing Continuous Evidence Surveillance

A future-proof SEM is not a static snapshot but a living evidence system. Continuous evidence surveillance automates the periodic re-execution of the SEM workflow to incorporate new research, enabling the map to remain current.

Operationalizing Surveillance:

• Automated Search Updates: Scheduled, automated searches are run against bibliographic databases (PubMed, Embase, Web of Science) using the original search strategy. RSS feeds, API alerts, and publisher feeds are integrated to capture new records in near real-time.
• Incremental Processing: Instead of reprocessing the entire corpus, the system performs incremental updates. New records flow through the modular pipeline, and only studies meeting inclusion criteria are added to the database. Version control tracks changes to the evidence base over time.
• Machine Learning Orchestration: Pre-trained machine learning models for screening and data extraction are automatically applied to new batches of studies. Confidence scores flag records for human reviewer attention, optimizing the human-in-the-loop process [1].
• Change Detection & Alerting: The system compares the updated map with the previous version to detect significant changes: the emergence of new evidence clusters, a substantial increase in studies on a particular chemical, or the publication of key study types (e.g., first human study). Automated alerts notify stakeholders of these relevant changes.

Workflow for continuous evidence surveillance in SEMs

Table 3: Protocol for a Continuous Evidence Surveillance Update Cycle

Stage	Action	Tools & Methods	Output & Quality Control
1. Trigger	Initiate update cycle.	Scheduled cron job OR trigger based on publication volume.	Audit log of cycle initiation.
2. Search	Re-execute saved search strategies.	Bibliographic database APIs (PubMed E-utilities, Elsevier, OVID).	File of new citation metadata; compare yield to expected volume.
3. Deduplication	Remove duplicates against existing SEM corpus.	Algorithmic matching (e.g., on DOI, title, author).	Log of duplicates removed; sample manual check.
4. Screening	Apply inclusion/exclusion criteria.	ML classifier pre-trained on previous decisions; human review of low-confidence predictions.	Set of included studies; measure classifier precision/recall.
5. Data Extraction	Populate coding fields.	NLP models for named entity recognition (chemicals, outcomes); human verification of key fields.	Structured data for new studies; inter-coder reliability checks.
6. Integration	Merge new data into live database.	Database merge scripts with versioning.	New database version tag; integrity checks.
7. Change Analysis	Compare evidence landscape to previous version.	Differential analysis scripts; generate metrics on growth, new clusters.	Surveillance report highlighting significant changes.

This automated, living approach transforms the SEM from a research product into a resilient surveillance system, a concept increasingly critical in fast-moving fields [82].

Building and maintaining a future-proof SEM requires a suite of specialized tools and resources. This toolkit spans software, platforms, and reference materials.

Table 4: Research Reagent Solutions for Advanced SEM Construction

Tool Category	Example Solutions	Primary Function in SEM Workflow
Evidence Synthesis Platforms	Rayyan, Covidence, EPPI-Reviewer, DistillerSR	Facilitates collaborative screening of abstracts/full-texts against inclusion criteria, with AI suggestions for acceleration [1].
Bibliographic & Search Tools	PubMed, Embase, Web of Science, Google Scholar, TOXLINE	Primary sources for comprehensive, systematic literature searching [1].
Automation & Machine Learning	ASReview (Active Learning), RobotReviewer, Custom NLP scripts (Python spaCy, SciBERT)	Reduces manual screening workload by prioritizing likely relevant studies and automating data extraction (e.g., chemical names, outcomes) [1].
Data Management & Versioning	Git/GitHub/GitLab, Dataverse, Open Science Framework (OSF)	Manages protocols, search strategies, and coding schemas; ensures transparency, reproducibility, and version control.
Visualization & Dissemination	Tableau, R Shiny, Python (Plotly, NetworkX), Interactive HTML/Javascript	Creates static and interactive visualizations (heatmaps, evidence gap maps, network graphs) for exploring and communicating the evidence map [1].
Chemical Intelligence	CompTox Chemicals Dashboard (EPA), PubChem, ChEMBL	Authoritative sources for chemical identifiers, structures, and properties, essential for normalizing chemical names across studies.

Application in Chemical Risk Assessment: A Framework for Integration

The true test of a future-proofed SEM is its seamless integration into regulatory and research workflows for chemical safety.

Operational Integration Pathways:

• Priority-Setting for Risk Assessment: Regulatory agencies can use SEMs to triage large chemical inventories (e.g., the TSCA Active Inventory). Maps can rank chemicals based on evidence volume, the presence of high-priority health outcomes (carcinogenicity, reproductive toxicity), or significant data gaps, directing assessment resources efficiently [2].
• Protocol Development for Systematic Reviews: An SEM provides the foundational scoping for a subsequent SR. It precisely defines the available evidence pool, refines the PECO question, and identifies the most relevant study designs and outcomes, making the SR process more efficient and targeted [2].
• Active Gap Identification for Research Funding: SEMs objectively identify under-studied chemicals, exposures, or health effects. This evidence can guide strategic research investments from public health agencies (e.g., NIEHS) or inform the development of tiered testing strategies.
• Horizon Scanning for Emerging Risks: Continuous surveillance SEMs can act as early-warning systems. By monitoring for new studies on chemicals of emerging concern (e.g., novel PFAS, plasticizers), they can alert regulators to potential risks before they become widespread public health issues.

Integration of SEMs into chemical risk assessment and priority-setting

Case Example - Phthalates: A regulatory agency could deploy a continuous SEM on phthalates. The initial map would catalog thousands of studies on various phthalates and health outcomes. Automated surveillance updates the map monthly. Analytics dashboards show a rapid increase in studies linking DINP to adipogenesis and liver effects. The system alerts managers, who use this intelligence to commission a rapid SR on that specific linkage, thereby accelerating the risk assessment process.

Future-proofing Systematic Evidence Maps is an architectural and methodological imperative for modern chemical risk assessment. By intentionally designing SEMs for scalability—through cloud-native, modular infrastructures—and continuous surveillance—via automation and machine learning—these tools can transition from costly, static projects into efficient, living evidence systems [81] [82]. This evolution directly addresses core challenges in chemical regulation: managing data volume, ensuring transparency, and making resource-efficient decisions [2].

The integrated framework presented here enables SEMs to serve as the central nervous system for evidence-informed chemical safety. They provide the foundational landscape analysis to prioritize assessments, guide rigorous syntheses, and strategically fill knowledge gaps. For researchers and regulatory professionals, investing in this future-proofed approach to evidence mapping is not merely a technical upgrade; it is a strategic commitment to building a more agile, responsive, and resilient foundation for public health protection in an era of constant scientific and regulatory flux.

Evaluating Systematic Evidence Maps: Comparative Analysis and Impact on Decision-Making

The field of chemical risk assessment is defined by a critical need to make reliable, transparent decisions based on a vast, complex, and often contradictory body of scientific evidence [2]. Regulatory bodies face the dual challenge of evaluating legacy chemicals while assessing new substances entering the market, all within constrained resources [2]. Traditional narrative approaches to reviewing evidence are prone to selection bias and lack transparency, undermining confidence in regulatory decisions [83] [2]. In this context, systematic methodologies have emerged as essential tools. Systematic Reviews (SRs) and Systematic Evidence Maps (SEMs) represent two pillars of modern evidence synthesis, each with distinct yet complementary roles [83] [84]. This whitepaper, framed within a broader thesis on advancing chemical risk assessment, delineates the technical specifications, applications, and synergistic relationship between SEMs and SRs, providing researchers and risk assessors with a guide for their effective deployment.

Core Definitions and Methodological Foundations

Systematic Review (SR): A Systematic Review is a rigorous, protocol-driven methodology designed to answer a specific, focused research question by identifying, appraising, and synthesizing all relevant empirical evidence [83] [84]. Its primary aim is to minimize bias and provide reliable findings to directly inform decision-making, such as determining the hazard potential of a specific chemical [2]. It is characterized by a structured framework (e.g., PECO/PICO: Population, Exposure/Intervention, Comparator, Outcome), a comprehensive search, critical appraisal of study quality, and often a quantitative synthesis (meta-analysis) [83] [2].

Systematic Evidence Map (SEM): A Systematic Evidence Map is a systematic method for characterizing and cataloging a broad evidence base. Its purpose is not to synthesize results or answer a specific risk question, but to visually represent the research landscape [83] [54]. An SEM identifies the quantity, distribution, and key characteristics of available research (e.g., types of studies, populations, exposures, outcomes measured), highlighting both evidence clusters and critical gaps [83] [2]. It serves as a tool for problem formulation, priority-setting, and guiding the efficient commissioning of future SRs or primary research [2] [54].

Table 1: Foundational Comparison of SEMs and Systematic Reviews

Aspect	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Purpose	To map the scope, volume, and characteristics of an evidence base; to identify gaps and trends [83] [2].	To answer a focused question by synthesizing evidence to determine the direction and strength of an effect or association [83] [84].
Research Question	Broad, exploratory (e.g., "What evidence exists on the health effects of chemical X?") [85].	Specific, definitive (e.g., "Does occupational exposure to chemical X increase the risk of outcome Y in adults?") [83].
Critical Appraisal	Typically does not involve formal risk-of-bias assessment of individual studies [83] [85].	Requires rigorous critical appraisal (risk-of-bias assessment) of each included study [2].
Data Synthesis	No quantitative or qualitative synthesis of results. Data is cataloged and presented descriptively, often in matrices or interactive databases [83] [54].	Integrates findings via qualitative synthesis and/or quantitative meta-analysis to generate an overall effect estimate [83] [84].
Key Output	Evidence inventory, gap analysis, visual research landscape, prioritized research questions [2] [54].	Qualitative summary, quantitative effect estimate (e.g., odds ratio), statement on strength of evidence, direct recommendations [83] [2].
Time & Resource Intensity	High, due to the breadth of the search and data extraction [85]. Can take 12+ months.	Very High, due to depth of appraisal and synthesis. Often takes 12-24 months [83] [2].

Functional Applications in Chemical Risk Assessment

Within the chemical risk assessment workflow, SEMs and SRs are applied at different stages to address distinct needs.

The Role of SEMs: SEMs are primarily problem-formulation and scoping tools. Regulatory programs, such as the U.S. EPA's Integrated Risk Information System (IRIS), use SEMs to inform assessment priorities, determine the need for updated assessments, and identify data gaps [54]. By providing a comprehensive overview, an SEM can reveal that while there may be hundreds of studies on a chemical, very few investigate a specific sensitive endpoint or exposure scenario, thereby guiding targeted research funding [2]. Furthermore, SEMs enable "evidence surveillance," allowing agencies to monitor emerging research trends efficiently [2].

The Role of SRs: SRs are the definitive tool for hazard identification and characterization when a risk management decision is required. They provide the transparent, bias-minimized synthesis necessary to establish a quantitative dose-response relationship or to conclude whether a chemical is a known or probable human carcinogen [2]. Their structured approach ensures all relevant evidence is considered, mitigating "cherry-picking" of studies [2].

Table 2: Application in Risk Assessment Workflow

Risk Assessment Stage	Role of Systematic Evidence Map (SEM)	Role of Systematic Review (SR)
Problem Formulation & Prioritization	Primary Tool. Scans broad evidence to determine if a full assessment is warranted, identifies key endpoints and populations, and sets the scope for a subsequent SR [2] [54].	Not typically used at this stage.
Hazard Identification	Precursor. Identifies all studies reporting on specific health outcomes for cataloging [54].	Definitive Tool. Appraises and synthesizes the evidence from identified studies to determine if a causal relationship exists [2].
Dose-Response Analysis	Informs which exposure metrics and outcomes have sufficient data for quantitative analysis [54].	Primary Tool. Synthesizes quantitative data to model the relationship between exposure and effect [2].
Evidence Surveillance & Update	Efficient Tool. Can be periodically updated to identify new research trends and determine if new evidence necessitates an SR update [2].	Resource-intensive to update; often relies on SEMs to trigger the decision to update.

Experimental Protocols and Methodological Specifications

The following protocols outline the core steps for conducting an SEM and an SR within a chemical risk assessment context.

Protocol for a Systematic Evidence Map (SEM)

1. Develop and Register a Protocol:

• Objective: Define the broad scope (e.g., "to map human, animal, and mechanistic studies on chemical X and neurological outcomes").
• Eligibility Criteria (PECO): Establish broad inclusion criteria. Population: Humans, animals, in vitro systems. Exposure: Chemical X and its major metabolites. Comparator: Any appropriate control. Outcomes: All neurological/behavioral endpoints. Study Types: All empirical studies.
• Search Strategy: Design a comprehensive search string for multiple databases (PubMed, Embase, Web of Science, TOXLINE). Plan to search grey literature.
• Register the protocol on a platform like PROSPERO or Open Science Framework.

2. Evidence Search and Retrieval:

• Execute the systematic search across all sources.
• Manage records using reference management software (e.g., EndNote, Covidence, DistillerSR).
• Document search results and dates.

3. Screening of Studies:

• Conduct screening in two phases:
  • Title/Abstract Screening: Apply broad eligibility criteria to exclude clearly irrelevant records.
  • Full-Text Screening: Apply detailed criteria to finalize the included study set.
• Use dual, independent screening with conflict resolution to minimize error.

4. Data Extraction and Coding:

• Extract descriptive data into a structured database or spreadsheet. Key fields include: study identifier, study design (human cohort, animal chronic, in vitro), population characteristics, exposure details (dose, route, duration), specific outcomes measured, and key findings (direction of effect, not a synthesized result).
• Do not perform risk-of-bias assessment or synthesize outcome data.

5. Evidence Mapping and Reporting:

• Categorize and summarize the extracted data.
• Generate visualizations: evidence atlases, heat maps showing volume of studies by outcome and study type, interactive databases.
• Report using relevant guidance (e.g., PRISMA-ScR extensions) and clearly highlight evidence clusters and gaps [83].

Protocol for a Systematic Review (with Meta-Analysis)

1. Develop and Register a Protocol:

• Objective: Pose a specific, answerable question (e.g., "What is the effect of oral exposure to chemical X on liver weight in adult rats?").
• Eligibility Criteria (PECO): Define narrow, explicit criteria. Population: Adult, male and female rats. Exposure: Oral gavage of chemical X. Comparator: Vehicle control. Outcome: Absolute or relative liver weight. Study Design: Controlled experimental studies.
• Register the protocol.

2. Evidence Search and Retrieval: (Identical in rigor to SEM, but may be more focused).

3. Screening of Studies: (Identical in process to SEM).

4. Data Extraction:

• Extract detailed data needed for synthesis and appraisal: study design specifics, sample sizes, mean outcome values, measures of variance (SD, SE), effect estimates, and covariates.

5. Critical Appraisal (Risk-of-Bias Assessment):

• Use a validated tool (e.g., SYRCLE's RoB tool for animal studies, ROBINS-I for observational studies) to assess the internal validity of each study [2].
• Perform dual, independent assessment.

6. Data Synthesis:

• Qualitative Synthesis: Tabulate and describe findings from all studies.
• Quantitative Synthesis (Meta-Analysis): Where studies are sufficiently homogeneous, pool effect sizes using appropriate statistical models (fixed- or random-effects).
  • Calculate an overall weighted effect estimate (e.g., mean difference) and confidence interval.
  • Assess statistical heterogeneity (I² statistic).
  • Create forest plots to visualize individual study and pooled results.
  • Investigate sources of heterogeneity via subgroup analysis (e.g., by sex, dose).

7. Report and Conclude:

• Report following PRISMA guidelines [83].
• Grade the overall confidence in the body of evidence (e.g., using GRADE).
• State clear conclusions relevant to the research question.

The Evidence Synthesis Ecosystem: A Visual Workflow

The complementary relationship between SEMs and SRs, and their position within the broader evidence synthesis landscape, can be visualized as a strategic workflow. The following diagram, generated using DOT language, illustrates how these tools interact from initial problem identification to final risk assessment decision.

Diagram 1: Strategic Workflow for Evidence Synthesis in Risk Assessment. This diagram illustrates the complementary pathways, with SEMs often serving as a critical scoping precursor to definitive SRs, while alternative review types address different resource or time constraints.

Conducting high-quality SEMs and SRs requires a suite of methodological tools and resources. The following table details key components of the modern evidence synthesis toolkit for chemical risk assessment.

Table 3: Research Reagent Solutions for Evidence Synthesis

Tool/Resource Category	Specific Examples & Platforms	Primary Function in SEM/SR
Protocol Registration & Guidance	PROSPERO, Open Science Framework (OSF), Cochrane Handbook, SRP-HA (SR for Protocol in Health Assessment)	Provides a platform to pre-register review protocols to reduce bias; offers authoritative methodological guidance [2].
Bibliographic Database Search	PubMed/MEDLINE, Embase, Web of Science, Scopus, TOXLINE, EPA's Health and Environmental Research Online (HERO)	Primary sources for executing comprehensive, reproducible literature searches as required by both SEM and SR [2].
Grey Literature Search	Regulatory agency websites (EFSA, EPA), clinical trial registries (ClinicalTrials.gov), dissertations (ProQuest), conference abstracts.	Ensures search comprehensiveness and mitigates publication bias by identifying unpublished or non-peer-reviewed studies [2].
Deduplication & Screening Software	Covidence, Rayyan, DistillerSR, EPPI-Reviewer, CADIMA	Manages the import, deduplication, and multi-phase screening of large volumes of search results using dual, independent reviewer workflows [84].
Data Extraction & Management	Custom Excel/Google Sheets templates, DistillerSR, SRDR+ (Systematic Review Data Repository)	Provides structured forms for consistent and accurate extraction of descriptive (SEM) or quantitative/qualitative (SR) data from included studies [2].
Risk-of-Bias Assessment Tools	ROBINS-I (observational studies), SYRCLE's RoB tool (animal studies), Cochrane RoB 2.0 (RCTs), NTP/OHAT approach	Standardized tools for critically appraising the internal validity of studies included in an SR; not typically used in SEMs [2].
Quantitative Synthesis (Meta-Analysis) Software	R packages (metafor, meta), Stata (metan), RevMan, Comprehensive Meta-Analysis	Performs statistical pooling of effect estimates, heterogeneity analysis, subgroup analysis, and generation of forest/funnel plots for SRs [83] [84].
Evidence Mapping & Visualization	EPPI-Mapper, Tableau, Microsoft Power BI, R (ggplot2, plotly), interactive HTML tables	Creates visual representations of the mapped evidence landscape for SEMs, such as heat maps, bubble plots, and evidence inventories [83] [54].
Reporting Guidelines	PRISMA (SRs), PRISMA-ScR (Scoping Reviews & SEMs), MOOSE (observational studies), ENTREQ (qualitative synthesis)	Checklists to ensure transparent, complete, and reproducible reporting of the review methods and findings [83].

Systematic Evidence Maps and Systematic Reviews are not competing methodologies but sequential and synergistic components of a robust evidence-based risk assessment paradigm [83] [2]. The SEM provides the essential wide-angle lens, efficiently surveying the terrain to identify where the most critical and answerable questions lie. The SR then provides the high-powered telescopic examination of those targeted areas, delivering the synthesized, appraised evidence necessary for definitive hazard characterization and risk management decisions [54].

For regulatory bodies and researchers navigating the expansive and growing literature on chemical hazards, embracing this complementary ecosystem is key to transparency, efficiency, and scientific credibility. Investing in SEMs as a problem-formulation and priority-setting tool ensures that the more resource-intensive SRs are deployed strategically where they are most needed, ultimately strengthening the foundation of public health and environmental protection.

The field of chemical risk assessment faces a formidable challenge: reconciling a vast and ever-growing body of scientific evidence with the urgent, resource-constrained needs of regulatory decision-making. Systematic review (SR) methods, while robust, are often ill-suited to this scale, being time-intensive and designed for tightly focused questions [2]. This tension has catalyzed the development and adoption of broader evidence synthesis methodologies, notably Systematic Evidence Maps (SEMs) and Scoping Reviews, which serve as critical tools for navigating complex evidence landscapes [2] [1].

Within the context of a thesis on systematic evidence maps, this whitepaper positions SEMs as a foundational, problem-formulation tool within chemical risk assessment workflows. Agencies like the U.S. Environmental Protection Agency (EPA) now routinely employ SEMs to support programs such as the Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) assessments [3] [30]. Their primary function is to provide a comprehensive, queryable overview of a broad evidence base—characterizing its extent, identifying trends, and highlighting critical knowledge gaps to guide future targeted systematic reviews or primary research [2]. Scoping reviews, while sharing a similar exploratory aim, often arise from different disciplinary traditions and can exhibit distinct methodological practices [86] [87].

For researchers, scientists, and drug development professionals, understanding the nuanced distinctions between these two methodologies is essential for selecting the appropriate tool. The choice hinges on the specific research objective: Is the goal to create a structured, interactive database of evidence for an entire chemical class (an SEM), or to systematically scope the nature and volume of literature on a broader operational or clinical topic (a Scoping Review)? This guide clarifies the terminology, demarcates methodological boundaries, and provides practical protocols to inform this critical decision.

Terminology and Conceptual Foundations

SEMs and Scoping Reviews are both systematic, transparent methods for cataloging and characterizing bodies of literature. However, their foundational purposes, standard outputs, and typical applications in scientific research differ in key aspects, as summarized in the table below.

Table 1: Key Characteristics of Systematic Evidence Maps (SEMs) and Scoping Reviews

Characteristic	Systematic Evidence Map (SEM)	Scoping Review
Primary Purpose	To create a structured database and visual overview of a broad evidence base; to identify specific evidence clusters and gaps for future synthesis [2] [30].	To examine the extent, range, and nature of research activity on a topic; to clarify key concepts and definitions [86] [87].
Typical Output	Interactive databases, structured evidence inventories, heatmaps, detailed methodologies for querying evidence [1] [30].	Narrative report with tabular and/or diagrammatic presentation of the scope of evidence, often identifying themes and characteristics [86] [88].
Core Question	"What evidence exists, and where are the precise densities and voids?" [2]	"What work has been conducted on this broad topic?" [86]
Risk of Bias Assessment	Conducted on a case-by-case basis, often for subsets of studies intended for further analysis [3] [30].	Not routinely performed; the focus is on mapping the evidence rather than appraising its quality [87].
Common Field of Application	Environmental health, chemical risk assessment, toxicology (e.g., EPA IRIS assessments) [2] [30].	Health services research, policy, social sciences, and broader public health topics [86] [89].
Theoretical Synthesis	Does not synthesize findings to answer a specific health question; synthesis is descriptive and categorical [1].	May include thematic analysis to identify patterns in how research is conducted, but does not synthesize quantitative health outcomes [86] [88].

Systematic Evidence Maps (SEMs) are defined as databases of systematically gathered research that characterize broad features of an evidence base [2]. In chemical risk assessment, they are explicitly designed as problem-formulation tools. Their value lies in providing a visual and interactive "map" that allows regulators and scientists to see the entire landscape of evidence for one or many chemicals, often tracked against various health outcomes and study types [30]. This enables forward-looking predictions, trend-spotting, and the efficient prioritization of resources for full systematic review [2].

Scoping Reviews follow a systematic process to map the key concepts and types of evidence underpinning a research area [86]. Their objective is often to identify the available literature, especially when a topic is complex or has not been comprehensively reviewed before. For example, a scoping review might be used to explore management practices for Good Manufacturing Practice (GMP) inspections or to catalog artificial intelligence applications in clinical trial risk assessment [86] [89]. The output is typically a narrative synthesis that categorizes the nature of the evidence (e.g., study designs, populations, methodologies) rather than the strength of the evidence for a specific outcome.

Demarcating Methodological Boundaries

While both methodologies share systematic steps—developing a protocol, conducting comprehensive searches, and screening studies—their application and depth at each stage reveal critical distinctions. The following workflow diagrams illustrate these processes.

Diagram 1: Systematic Evidence Map (SEM) Workflow (78 characters)

Diagram 2: Scoping Review Methodology Flowchart (82 characters)

Table 2: Comparison of Methodological Steps

Methodological Step	Systematic Evidence Map (SEM)	Scoping Review
Protocol & Question	Uses a broad PECO (Population, Exposure, Comparator, Outcome) statement to capture all potentially relevant evidence [30]. Specific aims focus on surveying core literature and identifying supplemental content (e.g., in vitro, NAMs) [30].	Often uses frameworks like SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) to define broader, exploratory questions [86] [88].
Search Strategy	Exhaustive, designed to capture the complete universe of relevant studies, often with no initial date restriction [30].	Comprehensive but may be pragmatically limited by the vast scope of the topic; often includes targeted grey literature searches [86] [87].
Screening & Eligibility	Dual-reviewer screening against the broad PECO. Studies are categorized as "PECO-relevant" (e.g., mammalian bioassays, epidemiology) or "supplemental" (e.g., mechanistic, toxicokinetic) [30].	Dual-reviewer screening against broader inclusion criteria focusing on topic relevance rather than specific study design for synthesis [86].
Data Extraction	Highly structured, using web-based forms to capture detailed metadata (e.g., chemical, dose, model system, health endpoint) for database creation and filtering [3] [30].	Charting of key information relevant to the scoping question (e.g., study design, country, key findings) [86] [88].
Critical Appraisal	Conducted selectively, if at all, often only on a subset of studies flagged for possible future systematic review [1] [30].	Typically not performed, as the goal is to map existing literature regardless of quality [87].
Synthesis & Output	Descriptive synthesis focused on cataloging and counting. Output is a searchable database/visualization (e.g., heatmaps, network diagrams) and a gap analysis report [1] [30].	Narrative and thematic synthesis to describe the scope of the field. Output is a report, often with conceptual diagrams or tables categorizing the evidence [86] [89].

Experimental Protocols in Practice

Protocol for a Systematic Evidence Map in Chemical Assessment

The following protocol is adapted from the standardized template used by the U.S. EPA IRIS Program [30].

1. Specific Aims:

• Survey Core Literature: Identify epidemiological and toxicological literature reporting health effects of exposure to the target chemical(s) as outlined by a broad PECO.
• Identify Supplemental Content: Catalog supplemental studies (in vitro, non-mammalian, toxicokinetic, New Approach Methodologies (NAMs)) to provide context on the available mechanistic and alternative-method evidence [30].
• Provide Visual Overview: Create interactive literature inventories (e.g., using Tableau or R Shiny) to visualize the distribution of studies by health system, study type, and species.
• Evaluate Studies (Optional): Conduct risk of bias evaluation on studies identified as potentially suitable for dose-response analysis.

2. Search Strategy:

• Develop search strings for multiple databases (e.g., PubMed, Embase, Scopus, TOXLINE).
• Utilize the EPA CompTox Chemicals Dashboard to identify synonyms and related compounds [30].
• No date or language restrictions are applied in the initial search.

3. Screening Process:

• Import references into systematic review software (e.g., DistillerSR, Rayyan).
• Two independent reviewers screen titles/abstracts, then full texts, against the PECO criteria.
• Conflicts are resolved by consensus or a third reviewer.

4. Data Extraction & Management:

• Use a pre-piloted, structured electronic form.
• Extract data on chemical, study design (species, sample size, exposure regimen), outcomes measured, and key results.
• All extracted data is stored in a relational database to enable complex querying and filtering.

5. Visualization and Reporting:

• Generate evidence tables and heatmaps showing the volume of evidence across health outcome categories.
• Publish an interactive web-based version of the map alongside a formal report detailing methods and gap analysis.

Protocol for a Scoping Review in Pharmaceutical Sciences

This protocol is modeled on a published scoping review of GMP inspection management [86] [88].

1. Research Question Development:

• Use the SPIDER framework to structure the question [86].
• Sample: Companies and inspectors involved in GMP inspections.
• Phenomenon of Interest: Management practices, outcomes, gaps, and challenges.
• Design: Academic and grey literature (regulatory reports, guidelines).
• Evaluation: Variations in practices and opportunities for enhancement.
• Research Type: Qualitative and mixed-methods studies.

2. Search Strategy:

• Search academic databases (PubMed, Embase) with tailored strings using Boolean operators.
• Systematically search for grey literature via Google Advanced, targeting specific regulatory agency websites (e.g., FDA, EMA, WHO) [86].

3. Eligibility & Selection:

• Apply inclusion/exclusion criteria (e.g., English language, recent time frame such as 2015-2025).
• Two reviewers independently screen titles/abstracts and full texts using software like Covidence [86].
• Data from included sources is charted in a standardized Excel template.

4. Data Synthesis:

• Employ thematic analysis to identify, analyze, and report patterns (themes) across the charted data.
• Develop a narrative summary describing the identified themes (e.g., pre-inspection, execution, and post-inspection phases) and present findings in conceptual diagrams [88].

The Scientist's Toolkit: Essential Materials and Platforms

Table 3: Research Reagent Solutions for Evidence Synthesis

Tool/Resource	Primary Function	Relevance to SEMs/Scoping Reviews
EPA CompTox Chemicals Dashboard	A curated database of chemical properties, identifiers, and related bioactivity data [30].	SEMs: Critical for developing comprehensive search strings, identifying related compounds, and accessing physicochemical data for the introduction [30].
Systematic Review Software (e.g., DistillerSR, Rayyan, Covidence)	Web-based platforms designed to manage the systematic review process, including reference import, dual screening, and data extraction [86].	Both: Essential for managing the screening and selection process with audit trails. Covidence was explicitly used in a scoping review protocol [86].
Machine Learning/AI Screening Tools (e.g., Sysrev, SWIFT-Review)	Platforms that use active learning or other AI models to prioritize references during title/abstract screening [87].	Both: Increases efficiency in screening large literature corpora. A scoping review on exposure tools used Sysrev's AI to predict inclusion likelihood [87].
Visualization Software (e.g., Tableau, R Shiny, Python Matplotlib/Plotly)	Tools for creating interactive dashboards, heatmaps, and network diagrams [1].	SEMs: Core to the output. Used to transform extracted data into queryable visual evidence maps [1] [30]. Scoping Reviews: Used for conceptual diagrams and summarizing study characteristics.
Grey Literature Search Protocol	A structured method for searching non-peer-reviewed sources (e.g., agency reports, theses, conference proceedings) [86].	Scoping Reviews: Often crucial for capturing policy and practice documents. A defined Google Advanced search strategy was a key component of a GMP review [86].

Within the domain of chemical risk assessment, Systematic Evidence Maps (SEMs) have emerged as indispensable strategic tools for research agencies and public health organizations. Framed within a broader thesis on evidence synthesis in toxicology, SEMs provide a structured, visual inventory of available scientific literature on a given chemical or group of chemicals [3] [4]. Their primary utility lies in informing problem formulation—the critical first phase of a risk assessment that defines the scope, key questions, and approach—and in supporting strategic priority setting for research and assessment activities [54]. Unlike a full systematic review, which synthesizes findings to answer a specific question, an SEM systematically catalogs and characterizes the existence and key features of evidence, highlighting its density, distribution, and gaps [90]. Agencies such as the U.S. Environmental Protection Agency (EPA) Integrated Risk Information System (IRIS) and the Agency for Toxic Substances and Disease Registry (ATSDR) now routinely employ SEMs to determine the need for new assessments, guide the scope of upcoming evaluations, and identify critical data deficiencies for emerging contaminants [3] [4] [54]. This technical guide delineates the core methodologies, applications, and validation of SEMs as foundational instruments for evidence-based decision-making in environmental health.

Core Methodology and Experimental Protocols

The construction of a robust SEM follows a protocol-driven, systematic process designed to maximize transparency, reproducibility, and utility for end-users. The following sections detail the standard experimental protocol as implemented by leading agencies [3] [30].

Defining the Scope: PECO Criteria and Supplemental Content

The foundation of an SEM is a clearly defined Population, Exposure, Comparator, and Outcome (PECO) statement. For hazard-based SEMs in chemical risk assessment, these criteria are kept intentionally broad to capture all potentially relevant literature [30].

• Population: Human populations (for epidemiological studies) and mammalian animal models (for toxicological studies).
• Exposure: The chemical or chemical group of interest.
• Comparator: Unexposed, vehicle-controlled, or alternatively exposed groups.
• Outcome: Any measured health effect.

Studies meeting these PECO criteria form the core evidence base. Additionally, SEMs track supplemental content to provide a complete landscape of available science [3] [30]. This includes:

• In vitro and non-mammalian model system studies.
• Toxicokinetic, ADME (Absorption, Distribution, Metabolism, and Excretion), and PBPK (Physiologically Based Pharmacokinetic) models.
• Studies reporting only exposure data without health outcomes.
• Evidence derived from New Approach Methodologies (NAMs), such as high-throughput screening or transcriptomic data [30].

Systematic Literature Search and Screening

A comprehensive, multi-database literature search is conducted using a pre-defined search strategy. The process employs standard systematic review practices, including the use of machine learning software for initial screening and, critically, dual independent review by two trained reviewers at both the title/abstract and full-text stages to minimize bias and error [3] [30]. A literature flow diagram (e.g., based on PRISMA guidelines) documents the screening process and results.

Data Extraction and Management

For each study that meets the PECO criteria, data are extracted into structured, web-based forms. Key extracted elements typically include [30]:

• Citation and study identifier.
• Study design (e.g., cohort, case-control, chronic bioassay).
• Test system (species, strain, cell line).
• Exposure regimen (route, duration, doses).
• Health systems and specific outcomes examined.
• Key results and points of departure (e.g., NOAEL, LOAEL).

This extracted data is stored in a relational database and made available in interactive, open-access formats, enabling users to filter and explore the evidence base according to their needs [3].

Evidence Mapping and Visualization

The "map" is created by categorizing and visualizing the extracted data. Studies are indexed across multiple dimensions. Effective visualization is paramount to an SEM's utility as a problem-formulation tool. The choice of chart type depends on the nature of the data and the story to be conveyed [60] [91].

Table 1: Data Visualization Types for Evidence Mapping

Chart Type	Best Use Case in SEMs	Key Advantage	Consideration
Evidence Gap Map (Heat Map)	Displaying the volume of evidence for combinations of outcomes and study types (e.g., human vs. animal).	Instantly reveals dense evidence clusters and critical gaps.	Can become cluttered with too many categories.
Bar/Column Chart	Comparing the number of studies across different categories (e.g., species, exposure routes).	Universally understood; excellent for precise comparison.	Limited in showing multi-dimensional relationships [60].
Interactive Database	Allowing users to filter evidence by multiple tags (chemical, outcome, study quality).	Provides the most detailed and flexible exploration of the catalog.	Requires platform development; not a static visual.
Flow Diagram	Documenting the literature search and screening process (PRISMA-style).	Ensures transparency and reproducibility of the SEM methods.	Describes process, not the evidence landscape itself.
Treemap	Showing the proportion of studies focused on different health effect categories (e.g., hepatic, renal, neurological).	Efficiently uses space to show part-to-whole relationships for hierarchical data [91].	Less precise for comparing similar-sized categories.

Study Evaluation (Optional)

A distinguishing feature of an SEM, as opposed to a full review, is that formal risk of bias or quality assessment is often optional. It may be conducted on a case-by-case basis, typically when the SEM aims to identify the most suitable studies for a subsequent dose-response analysis [30]. When performed, it uses standardized tools tailored for epidemiological or toxicological study designs.

Below is a Graphviz diagram illustrating the sequential workflow and decision points in creating a Systematic Evidence Map.

Systematic Evidence Map (SEM) Creation Workflow.

Application in Problem Formulation and Priority Setting

The value of an SEM is realized through its direct application to the strategic challenges faced by agencies. It transforms a vast, unstructured body of literature into an actionable intelligence asset.

1. Informing Problem Formulation for Risk Assessments: For programs like EPA IRIS, an SEM is the foundational step in developing an Assessment Plan. By visualizing the evidence, assessors can determine which health outcomes have sufficient data for a full systematic review and dose-response analysis. It helps decide whether to assess a chemical as a single entity or as a group, and which exposure routes and durations are supported by evidence [54]. This ensures the subsequent, resource-intensive review focuses on answerable questions with available data.

2. Setting Strategic Priorities: SEMs provide an objective basis for portfolio management. Agencies can compare evidence landscapes across multiple chemicals to identify which have the most pressing data needs, the greatest potential for new hazard identification, or the largest public health impact given exposure potential. This supports decisions about which chemicals to assess next or where to direct research funding [4] [54].

3. Identifying Critical Data Gaps for Emerging Chemicals: For chemicals of emerging concern (e.g., novel PFAS), a rapid SEM can outline what is known and unknown. This gap analysis is crucial for triggering targeted research initiatives to generate data on specific endpoints, exposure scenarios, or susceptible life stages, thereby efficiently building the knowledge base needed for future risk assessment [3].

4. Supporting Evidence Surveillance and Read-Across: A living SEM can be updated periodically to monitor the evolution of the science. This surveillance function alerts agencies to new, pivotal studies that may warrant an updated assessment. Furthermore, the structured data in an SEM facilitates read-across strategies by allowing scientists to easily find studies on structurally similar chemicals for which data is sparse [54].

The following diagram maps the classification logic for studies identified in a literature search, demonstrating how an SEM organizes evidence for analysis.

Evidence Classification Logic in an SEM.

The Scientist's Toolkit: Research Reagent Solutions for SEM Implementation

Constructing a rigorous SEM requires both methodological frameworks and practical software tools. The following table details key "research reagents" for implementing SEMs in chemical risk assessment.

Table 2: Essential Toolkit for Systematic Evidence Mapping

Tool Category	Specific Item/Software	Function in SEM Process	Notes & Examples
Protocol & Framework	PECO Statement Template	Defines the scope of the literature search and inclusion criteria for the core evidence base [30].	The cornerstone of the SEM; must be finalized before any search begins.
	EPA SEM Methods Template [3] [30]	Provides a harmonized, step-by-step guide for conducting an SEM, ensuring consistency and best practices.	Published by EPA ORD; includes example language and adaptable modules.
Literature Management	Systematic Review Software (e.g., DistillerSR, Rayyan, Covidence)	Manages the import of search results, facilitates dual-independent screening at title/abstract and full-text levels, and tracks reasons for exclusion.	Essential for ensuring a transparent, auditable process. Some integrate machine learning for priority screening.
	Reference Manager (e.g., EndNote, Zotero)	Stores and deduplicates bibliographic records from multiple database searches.	Often used in conjunction with specialized review software.
Data Extraction & Management	Structured Web-Based Extraction Forms	Provides a consistent, digital interface for reviewers to extract predefined data points from full-text studies [30].	Can be built using survey platforms (e.g., REDCap) or within systematic review software. Ensures data integrity.
	Relational Database (e.g., PostgreSQL, MS Access) or Flat File System	Stores extracted data in a queryable format for analysis and visualization.	The backend that powers interactive evidence inventories and visualizations.
Visualization & Analysis	Business Intelligence Tools (e.g., Tableau, Power BI)	Creates interactive dashboards and evidence gap maps from the extracted database. Allows users to filter by chemical, outcome, study type, etc.	Key for translating the data catalog into a user-friendly, strategic tool.
	Programming Libraries (e.g., R ggplot2, Python Matplotlib/Seaborn)	Generates static publication-quality visualizations (bar charts, heatmaps) for reports.	Offers maximum customization for complex visualizations.
Chemical Intelligence	EPA CompTox Chemicals Dashboard	Provides curated data on chemical properties, identifiers, and associated bioassay data, used to inform the SEM introduction and context [30].	Critical for understanding the chemical(s) of interest and related structures.

Systematic Evidence Maps represent a paradigm shift in evidence management for chemical risk assessment. By providing a rigorously compiled, visually accessible, and interactive overview of the scientific landscape, they transform problem formulation and priority setting from subjective exercises into transparent, data-driven processes. They allow agencies like the EPA and ATSDR to strategically allocate limited assessment resources, precisely define the scope of complex evaluations, and communicate evidence gaps to the research community. As a component of a broader thesis on systematic review methodologies, the SEM validates its utility not by providing final answers, but by ensuring the right questions are asked first. The continued development and harmonization of SEM templates and practices promise greater efficiency and collaboration across the environmental health sciences, ultimately leading to more timely and protective public health decisions [54].

The Evolving Regulatory Frameworks: REACH and TSCA

Global chemical regulations are dynamic systems that balance hazard identification, risk management, and technological innovation. The European Union's REACH regulation and the United States' Toxic Substances Control Act (TSCA) represent two cornerstone frameworks, both of which are undergoing significant changes that redefine the role of scientific evidence in decision-making [92] [93].

The 2025 REACH Revision: The EU's REACH regulation is being revised with final legislation expected in late 2025 [93]. The update aims to modernize and streamline the regulation while strengthening protections. Key proposed changes include:

• Registration Validity and Data Requirements: Introduction of a 10-year validity period for registrations, coupled with more stringent completeness checks by the European Chemicals Agency (ECHA) [93]. Testing proposals will be required for all in vivo tests and complex endpoints, extending this requirement to lower tonnage bands (1-100 tonnes/year) [93]. The revision also proposes to formally integrate assessment of substances that are persistent, mobile, and toxic (PMT), very persistent and very mobile (vPvM), and Endocrine Disruptors (EDs) into the Chemical Safety Assessment [93].
• New Assessment Paradigms: A major innovation is the introduction of a Mixture Assessment Factor (MAF) for substances registered at over 1000 tonnes/year. This factor aims to account for combined exposure to multiple chemicals, moving towards a more realistic aggregate risk assessment [93].
• Expanded Scope: The revision will introduce obligatory notification for all polymers manufactured or imported at ≥1 tonne/year and a registration requirement for polymers identified as requiring registration (PRR) [93].

TSCA Implementation Under a New Administration: The implementation of the Frank R. Lautenberg Chemical Safety for the 21st Century Act is entering a new phase in 2025, with a shift in policy direction under the Trump administration [92]. Key developments include:

• Policy Reorientations: The new EPA is expected to revise the TSCA risk evaluation framework rule, potentially returning to policies from the first Trump administration [92]. Highlighted priorities include ensuring risk evaluations are "risk-based" rather than "hazard-based," focusing on exposure pathways not covered by other statutes, and applying real-world use scenarios, including the assumption that workers use required personal protective equipment (PPE) [92].
• Ongoing Mandates and Adjustments: Core TSCA obligations continue, including risk evaluations for approximately 20 high-priority substances and the PFAS reporting rule under Section 8(a)(7) [92]. However, the EPA has proposed significant exemptions to the PFAS reporting requirement to reduce burden, including a de minimis level (0.1% concentration), exemptions for PFAS in imported articles, and exclusions for byproducts and R&D chemicals [94].
• Funding and Personnel: While a continuing resolution increased EPA's environmental program funding slightly to $3.195 billion for IT modernization, deep staffing cuts are anticipated, which could impact the pace of chemical reviews [92]. The risk management rule for trichloroethylene (TCE) has seen its effective date for exemption requirements postponed to November 2025 due to ongoing litigation [95].

Table 1: Comparative Overview of Key Regulatory Changes in 2025

Regulatory Aspect	REACH (EU)	TSCA (US)
Primary 2025 Development	Major legislative revision [93].	Policy reorientation under new administration [92].
Core Scientific Focus	Introducing Mixture Assessment Factor (MAF); integrating PMT/vPvM/ED assessment [93].	Shifting to "risk-based" evaluations; focusing on uncovered exposure pathways [92].
Data & Testing	Testing proposals for in vivo tests extended to lower tonnage bands [93].	Promotion of New Approach Methodologies (NAMs) to reduce vertebrate animal testing [96].
Compliance & Burden	Increased demands (e.g., polymer notification) alongside streamlining goals [93].	Proposed exemptions (e.g., PFAS reporting) to reduce burden [94]; potential delays from staffing cuts [92].

Systematic Evidence Maps (SEMs): A Foundational Methodology

In the context of these complex and data-intensive regulatory landscapes, Systematic Evidence Maps (SEMs) emerge as a critical tool for evidence-based decision-making. An SEM is a database of systematically gathered research that characterizes broad features of an evidence base, designed to provide a comprehensive, queryable summary of policy-relevant research [2].

Contrast with Systematic Review (SR): While a Systematic Review aims to synthesize evidence to answer a specific, narrow question (e.g., "Does chemical X cause cancer in humans?"), an SEM is designed to scope and describe a much broader evidence landscape [2]. An SR is time and resource-intensive, suitable for definitive conclusions on prioritized issues. An SEM, in contrast, efficiently maps the available science—identifying what studies exist, on which chemicals, and for what health outcomes—to inform priority-setting, guide future targeted SRs, and highlight critical data gaps [2] [3].

Core Protocol for SEM Development: The U.S. EPA has standardized methods for developing SEMs to support programs like the Integrated Risk Information System (IRIS) [3].

• Problem Formulation & PECO Development: Define the map's scope. Populations, Exposures, Comparators, and Outcomes (PECO) criteria are kept broad to identify all potentially relevant mammalian bioassays and epidemiological studies [3].
• Comprehensive Search & Screening: Execute a systematic search across multiple scientific databases. Screening (typically by two independent reviewers) filters records for relevance [3]. Machine learning software may be used to assist in screening large volumes of references [3].
• Data Extraction & Curation: For studies meeting the PECO criteria, key data (study design, health system assessed) are extracted into structured forms [3]. The SEM also tracks supplemental information, including in vitro studies, New Approach Methodologies (NAMs) data, and pharmacokinetic models [3].
• Visualization & Analysis: Extracted data is presented in interactive visual formats (e.g., heat maps, evidence atlases) to illustrate the distribution and density of evidence across chemicals and outcomes, making gaps and clusters readily apparent [2] [3].

Table 2: Key Phases in Systematic Evidence Map Development [3]

Phase	Key Activities	Regulatory Science Utility
1. Planning & Scoping	Develop broad PECO; plan for supplemental data (NAMs, in vitro).	Ensures the map aligns with regulatory problem formulation and captures emerging science.
2. Search & Screening	Execute transparent search strings; dual-reviewer screening often assisted by AI.	Maximizes reproducibility and minimizes selection bias in evidence identification.
3. Data Extraction & Curation	Extract structured data on study design; curate NAMs and other supplemental data.	Creates a queryable database that links traditional and new toxicity data.
4. Visualization & Reporting	Generate interactive evidence atlases and gap analysis maps.	Supports stakeholder communication, priority-setting, and trend identification.

Application of SEMs to Chemical Alternatives Assessment

Chemical Alternatives Assessment (AA) is a systematic process to evaluate and compare potential substitutes for chemicals of concern, aiming to avoid "regrettable substitutions" with more hazardous alternatives [97]. It integrates hazard, exposure, performance, and economic viability assessments [98]. SEMs directly enhance the scientific robustness and efficiency of this process.

Informing Safer Chemical Design: By mapping the existing hazard and toxicokinetic data for a chemical of concern and its potential alternatives, an SEM provides assessors with a rapid, comprehensive overview of the available science. This supports the hazard assessment step, which is foundational to frameworks like the IC2 Alternatives Assessment Guide [98]. For instance, an SEM can quickly reveal if an alternative chemical has a well-studied toxicity profile or is a "data-poor" substance, guiding subsequent testing strategies.

Prioritizing and Identifying Data Gaps: Regulatory drivers like REACH's Authorization process and TSCA's risk management rules create urgent needs for alternatives [97] [95]. SEMs enable regulators and companies to efficiently triage large groups of chemicals. They can identify which alternatives have sufficient data for a comparative assessment and which require the generation of new data, ensuring resources are allocated to the most critical gaps [2].

Integrating New Approach Methodologies (NAMs): A core strength of the modern SEM protocol is the explicit tracking of NAMs data [3]. As EPA and other agencies promote NAMs—including in vitro assays, in silico models, and read-across approaches—to reduce vertebrate animal testing [96], SEMs become the essential tool for organizing and accessing this evidence. An SEM can correlate traditional animal study outcomes with high-throughput screening data for a class of chemicals, building confidence in the use of NAMs for future AAs of data-poor substances.

The following diagram illustrates the integrative role of Systematic Evidence Maps in supporting chemical alternatives assessment within the broader regulatory workflow.

• IC2 Alternatives Assessment Guide (v1.2): A comprehensive framework providing stepwise methodologies for conducting alternatives assessments, incorporating hazard, exposure, performance, and economic analyses [98].
• EPA's List of New Approach Methodologies (NAMs): A curated and regularly updated list of alternative test methods and strategies (e.g., QSAR, read-across, in vitro assays) recognized by EPA for use in TSCA decisions to reduce vertebrate animal testing [96].
• EPA Systematic Evidence Map (SEM) Template: The agency's standardized protocol for developing SEMs, detailing workflows for searching, screening, extraction, and visualization specifically for human health assessments [3].
• ECHA's REACH Guidance: The official source for updated data requirements, testing strategies, and compliance procedures under the REACH regulation, crucial for understanding EU obligations [93].
• Chemical Hazard Assessment Databases (e.g., GreenScreen): Standardized tools and associated databases for profiling and comparing the inherent hazards of chemicals, a key component of the hazard assessment phase in AAs [98].

The following diagram outlines the core, iterative steps in a chemical alternatives assessment process, highlighting key decision points.

Within the evolving discipline of evidence-based toxicology, Systematic Evidence Maps (SEMs) have emerged as a critical tool for navigating expansive and complex scientific literature. An SEM is formally defined as a queryable database of systematically gathered research, which extracts and structures data or metadata from a broad evidence base for exploration [8]. This methodology stands distinct from a Systematic Review (SR), which aims to synthesize evidence to answer a tightly focused research question. Instead, SEMs provide a comprehensive overview, characterizing the volume, distribution, and key features of available evidence to identify trends, clusters, and critical gaps [2].

In chemical risk assessment and pharmaceutical development, the application of SEMs addresses a fundamental challenge: the sheer volume and heterogeneity of data. The evidence base encompasses mammalian and non-mammalian in vivo studies, epidemiological research, in vitro assays, high-throughput screening data, and toxicogenomic studies [3]. SEMs provide a transparent and structured framework to organize this evidence, supporting critical functions such as problem formulation, hypothesis generation, and priority-setting for future systematic reviews or primary research [2] [3]. Their role is particularly vital for regulatory initiatives like the US EPA's Integrated Risk Information System (IRIS) and the EU's REACH, where efficiently characterizing evidence for numerous chemicals is essential [2]. This guide establishes the core criteria for evaluating the quality and utility of SEMs, ensuring they fulfill their potential as robust tools for evidence-informed decision-making.

Table 1: Core Distinctions Between Systematic Evidence Maps and Systematic Reviews

Feature	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Objective	To systematically catalog and characterize the extent, distribution, and key features of an evidence base [1] [2].	To answer a specific research question via synthesis of evidence, providing a summary estimate of effect or risk [2].
Research Question	Broadly scoped to capture a wide landscape of evidence [8].	Narrowly focused, typically defined by a PECO/PICO statement [2].
Synthesis	Does not synthesize findings to estimate effects; focuses on descriptive characterization [2].	Conducts qualitative, quantitative, or integrative synthesis of results from included studies.
Critical Appraisal	May be conducted selectively to characterize the distribution of study reliability, but is not mandatory [1].	A mandatory core component to assess risk of bias and interpret synthesized findings [2].
Key Output	Interactive databases, visual maps (e.g., heatmaps, network diagrams), and reports highlighting evidence clusters and gaps [1].	A synthesized summary of findings with an assessment of the confidence or certainty in the evidence [2].

A Framework for Evaluating Systematic Evidence Maps

The quality and usefulness of an SEM are not inherent but are determined by adherence to rigorous methodological standards and the functional utility of its outputs for its intended audience. Assessment criteria can be categorized into foundational methodological pillars and output-specific utility metrics.

Foundational Methodological Pillars

These criteria evaluate the integrity of the process used to create the SEM. A high-quality map is built on a foundation of transparency, reproducibility, and minimized bias.

• Protocol & Pre-registration: The development and public registration of a detailed, a priori protocol is paramount [2]. This should clearly state the map's objectives, the search strategy (including databases, date limits, and syntax), explicit eligibility criteria (e.g., PECO statements), and the planned data coding and extraction strategy [3]. Adherence to the protocol guards against arbitrary decision-making.
• Comprehensive & Reproducible Search: The search strategy must be designed to capture a broad and representative sample of the relevant evidence with minimal bias [2]. Searches across multiple bibliographic databases, grey literature sources, and clinical trial registries are standard. The exact search strings must be documented so the search is fully reproducible [1].
• Screening & Data Extraction Rigor: Screening of abstracts/full texts and data extraction should be performed by at least two independent reviewers, with a process for resolving conflicts [3]. The use of specialized systematic review software (e.g., EPPI-Reviewer, DistillerSR) or machine-learning-assisted tools to manage and document this process enhances consistency and auditability [3].
• Data Structure & Management: The chosen data model must accommodate the complexity and interconnectedness of chemical risk assessment data. Traditional flat tables may be insufficient; modern approaches advocate for knowledge graph structures, which offer flexible, schema-less storage of highly connected entities (e.g., linking chemicals, molecular targets, outcomes, and study designs) [8]. This facilitates more sophisticated querying and analysis.

Utility Metrics for Outputs and Impact

These criteria assess the final product and its value to end-users, such as regulators and research directors.

• Clarity of Evidence Landscape: The primary output should clearly answer: What evidence exists, where, and what are its general characteristics? Effective visualizations, such as interactive heatmaps showing study density by chemical and outcome, or bibliometric network diagrams, are essential for rapidly conveying this landscape [1].
• Identification of Critical Gaps & Clusters: A successful map must do more than list studies; it must synthesize patterns to pinpoint specific evidence gaps (e.g., a lack of chronic exposure data for a high-production-volume chemical) and evidence clusters (e.g., numerous in vitro studies on a specific pathway) that warrant further investigation [2].
• Support for Decision-Making: The map must be fit-for-purpose. In a regulatory context, this means directly informing the problem formulation phase of a risk assessment, helping to determine whether a full systematic review is justified, or prioritizing chemicals for evaluation [3].
• Accessibility & Interoperability: The map's data should be publicly accessible in open, machine-readable formats (e.g., CSV, JSON-LD for graphs) [3]. An interactive web interface that allows users to filter and query the underlying database significantly enhances utility [8]. Interoperability with other data systems (e.g., chemical registries, adverse outcome pathway databases) is a mark of advanced design.

Table 2: Success Metrics for Evaluating an Evidence Map

Evaluation Dimension	Key Performance Indicators (KPIs)	Assessment Method
Methodological Rigor	1. Existence of a publicly accessible protocol.2. Documented, reproducible search strategy.3. Dual-independent review process with reported agreement statistics (e.g., Cohen's Kappa).4. Use of a structured, auditable data management platform.	Review of published materials and supplemental documentation.
Comprehensiveness & Bias	1. Number and relevance of databases searched.2. Proportion of grey literature included.3. Flow diagram accounting for all identified records.4. Analysis of temporal and geographic trends in the evidence base.	Analysis of the study flow and characteristics of the included dataset.
Output Utility	1. Generation of clear, actionable evidence gaps and clusters.2. Development of interactive visualizations or query tools.3. Demonstrated use in a decision-making context (e.g., cited in a risk assessment problem formulation).4. User feedback from target audience (e.g., regulators).	Review of map reports and outputs; citation analysis; stakeholder surveys.
Data Accessibility & Reuse	1. Public availability of the coded dataset.2. Provision of an interactive online interface.3. Use of standard vocabularies or ontologies (e.g., MeSH, ChEBI) to enhance interoperability.	Check for data repositories (e.g., Figshare, Zenodo) and live web tools.

Experimental Protocols for Evidence Map Generation

The generation of a robust SEM follows a standardized, multi-stage workflow. The following protocol, synthesized from established guidance and the U.S. EPA's template, details the critical steps [1] [3].

Protocol Development and Registration

• Objective: Define the map's scope and methodology prospectively to minimize bias.
• Procedure:
  • Formulate Broad Question: Develop a guiding question (e.g., "What is the available evidence on the neurotoxicity of organophosphate flame retardants?").
  • Develop PECO Criteria: Define broad Population/Planet, Exposure, Comparator, and Outcome criteria to guide search and screening [3]. For chemical assessments, "Population" may include human, animal, and in vitro models.
  • Design Search Strategy: Identify relevant databases (e.g., PubMed, Embase, Scopus, TOXLINE). Develop search strings using chemical names, CAS numbers, and broad outcome terms. Plan for grey literature searching.
  • Define Coding Strategy: Design the data extraction form (codebook). Key fields include citation details, chemical(s) studied, study design (e.g., human cohort, rodent chronic bioassay, in vitro assay), health system assessed, exposure regimen, and outcomes measured.
  • Register Protocol: Publish the protocol on a platform like PROSPERO or the Open Science Framework.

Search and Screening Implementation

• Objective: Identify all potentially relevant records with high sensitivity.
• Procedure:
  • Execute Search: Run the finalized searches across all planned sources. De-duplicate records using reference management software.
  • Pilot Screening: Calibrate the review team by pilot-testing the eligibility criteria on a random sample of 50-100 records. Refine criteria as needed.
  • Title/Abstract Screening: Two independent reviewers screen each record against the PECO criteria. Conflicts are resolved by consensus or a third reviewer. Software platforms like Rayyan or Covidence streamline this process.
  • Full-Text Screening: Retrieve and screen the full text of all records passing the initial stage using the same dual-review process. Document reasons for exclusion at this stage.

Data Extraction and Coding

• Objective: Systematically transform unstructured study information into structured, analyzable data.
• Procedure:
  • Calibrate Extraction: Reviewers independently extract data from a common subset of studies (e.g., 5-10) to ensure consistency.
  • Structured Extraction: Using the pre-defined codebook, extract data for each included study. The EPA template emphasizes tracking not only core PECO studies but also supplemental content such as New Approach Methodologies (NAMs), pharmacokinetic data, and genotoxicity studies [3].
  • Quality Control: A senior reviewer audits a random sample (e.g., 10%) of completed extraction forms for accuracy and completeness.

Study Evaluation and Data Visualization

• Objective: Characterize the evidence base and generate accessible insights.
• Procedure:
  • Critical Appraisal (Optional): Depending on the map's purpose, a risk of bias assessment (e.g., using OHAT, SYRCLE tools) may be conducted to characterize the distribution of study reliability [1].
  • Database Creation: Populate a database or knowledge graph with the extracted and coded data [8].
  • Generate Visualizations: Create static and interactive visualizations. Common outputs include:
    • Evidence Atlases: Interactive tables listing all studies with filterable columns.
    • Heatmaps: Visualizing study density across two dimensions (e.g., chemical vs. health outcome).
    • Flow Diagrams: Illustrating the study selection process.
  • Synthesize Patterns: Analytically describe the evidence landscape, explicitly stating identified evidence clusters and gaps.

Systematic Evidence Map Generation Workflow

Visualization of Evidence Classification and Interrelationships

Effective visualization is the conduit through which the structured data of an SEM conveys insight. Beyond simple counts, diagrams can reveal the taxonomic structure of the evidence and the functional relationships between its elements, which are crucial for chemical risk assessment.

A central organizing principle is the classification of study types. This hierarchy determines how evidence is categorized, queried, and weighted for different assessment purposes. The following diagram illustrates a standard classification system adapted for chemical risk assessment, aligning with EPA practices [3].

Evidence Classification Hierarchy for Chemical Risk

The true power of an SEM is realized when these classified entities are connected to show a network of evidence. A knowledge graph model moves beyond a static hierarchy to a dynamic web of relationships [8]. For example, a specific chemical entity (e.g., Bisphenol A) can be linked to multiple molecular target entities (e.g., Estrogen Receptor alpha), each supported by several in vitro study entities. Those targets are then linked to potential adverse outcome entities (e.g., mammary gland hyperplasia), which are investigated by animal bioassay entities. This graph structure allows for sophisticated queries, such as "Show all chemicals with evidence linking them to both ERα activation and mammary gland effects," directly informing the development of Adverse Outcome Pathways (AOPs) and mode-of-action analyses.

The Researcher's Toolkit for Evidence Mapping

Producing a high-quality SEM requires a suite of specialized tools to manage the volume of literature and the complexity of data. The following toolkit categorizes essential solutions, emphasizing software that enables transparency, collaboration, and advanced data structuring.

Table 3: Research Reagent Solutions for Evidence Mapping

Tool Category	Example Solutions	Primary Function in SEM
Protocol Registration	PROSPERO, Open Science Framework (OSF)	Provides a public, time-stamped record of the map's planned methods, enhancing transparency and reducing reporting bias.
Reference Management & Deduplication	EndNote, Zotero, Rayyan	Stores retrieved citations, identifies and removes duplicate records from multiple database searches, and facilitates initial screening.
Systematic Review Management	DistillerSR, EPPI-Reviewer, Covidence, Rayyan	Web-based platforms that manage the entire workflow: importing references, facilitating dual-independent screening and data extraction with conflict resolution, and exporting structured data.
Machine Learning / Text Mining	SWIFT-Review, Abstractxr, ASReview	Uses active learning to prioritize records during screening, potentially reducing the manual screening workload by identifying irrelevant studies with high sensitivity.
Data Extraction & Coding	Custom Google Sheets/Excel forms, REDCap, integrated forms in SR software (e.g., DistillerSR)	Provides a structured interface (codebook) for reviewers to consistently extract and code predefined data points from each study.
Data Storage & Analysis (Advanced)	Graph Databases (Neo4j, Amazon Neptune), R/Python with tidyverse/pandas	Stores coded data in flexible, interconnected knowledge graphs rather than flat tables, enabling complex querying of relationships between chemicals, outcomes, and studies [8].
Visualization & Reporting	R (ggplot2, plotly), Python (matplotlib, seaborn), Tableau, Evidence Mapping Tools in EPPI-Reviewer	Generates static and interactive visualizations (heatmaps, bar charts, network diagrams) and helps synthesize findings into reports and interactive web applications.

Systematic evidence maps (SEMs) represent a transformative methodological advancement for organizing and characterizing broad bodies of environmental health research, particularly within chemical risk assessment. This technical guide elucidates the formal integration of SEMs into structured Evidence-to-Decision (EtD) processes, framing this evolution within a broader thesis on evidence synthesis in regulatory science. SEMs serve as critical problem-formulation and priority-setting tools by systematically cataloging and visualizing the available evidence, thereby informing which specific questions merit subsequent full systematic review or require new primary research [2] [3]. We detail the standardized methodology for constructing SEMs, demonstrate their role in streamlining Quantitative Risk Assessments (QRAs), and present a replicable workflow for embedding SEM outputs into formal EtD frameworks. This integration enhances the transparency, efficiency, and reliability of regulatory decisions by ensuring that risk management priorities and actions are grounded in a comprehensive, bias-minimized overview of the extant science [54].

Chemical risk assessment is confronted by a rapidly expanding and disparate evidence base, encompassing traditional in vivo studies, epidemiological data, and New Approach Methodologies (NAMs) like high-throughput screening and in sil models [2]. Systematic reviews (SRs) have been adopted from clinical medicine to synthesize evidence for specific, focused questions but are often too resource- and time-intensive for initial problem scoping in regulatory contexts [2]. This creates a critical gap in the evidence-to-decision pipeline.

Systematic evidence maps address this gap. They are defined as databases of systematically gathered research that characterize broad features of an evidence base—such as the chemicals studied, health outcomes investigated, and model systems used—without performing a full synthesis or meta-analysis [2] [3]. Their primary function is to provide a queryable overview that supports evidence surveillance, trend identification, and the strategic planning of future research or targeted SRs [2]. Within the broader thesis of advancing chemical risk assessment, SEMs are posited as the essential first layer of evidence organization, enabling a more efficient and rational allocation of resources for subsequent, deeper analysis in the EtD process [54].

Core Methodology: Constructing a Systematic Evidence Map

The construction of an SEM follows a rigorous, protocol-driven process adapted from systematic review standards to maximize transparency and reproducibility. The U.S. EPA's Integrated Risk Information System (IRIS) program has developed a standardized template that exemplifies this methodology [3].

Protocol and PECO Development: The process begins with a pre-published protocol. The Population, Exposure, Comparator, Outcome (PECO) criteria are deliberately kept broad to capture all potentially relevant mammalian animal bioassays and epidemiological studies for human hazard identification [3]. Supplemental tracking is also established for evidence from in vitro models, pharmacokinetic data, and NAMs [3].

Search, Screening, and Data Extraction: A comprehensive, multi-database literature search is executed. Screening is typically performed by two independent reviewers to minimize error and bias [3]. Specialized software, sometimes incorporating machine learning for prioritization, is used to manage this process. Data from included studies are extracted into structured, web-based forms, capturing key study design elements and health systems assessed [3].

Study Evaluation and Output: Critical appraisal of individual studies may be conducted on a case-by-case basis depending on the SEM's purpose [3]. The final output is not a synthesized conclusion but an interactive database and visualizations (e.g., evidence atlases, heat maps) that allow users to explore the distribution and characteristics of the evidence [2] [3].

Table 1: Key Characteristics of Systematic Evidence Maps vs. Systematic Reviews

Feature	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Objective	To catalog, characterize, and visualize the scope of an evidence base [2].	To answer a specific research question via evidence synthesis and meta-analysis [2].
PECO Scope	Broadly defined to capture maximum relevant evidence [3].	Precisely and narrowly defined for a focused question [2].
Data Synthesis	Not performed; results are descriptive and visual.	Required; includes qualitative and/or quantitative synthesis (meta-analysis).
Critical Appraisal	May be conducted selectively or at a high level [3].	Mandatory and rigorous for all included studies [2].
Output	Interactive database, evidence gap maps, trend analyses [2] [3].	Qualitative summary, quantitative effect estimates, certainty ratings (e.g., GRADE).
Role in EtD Process	Problem formulation, priority-setting, informing the need for an SR [54].	Directly informing risk estimates and safety conclusions for decision-making [2].

Diagram: Systematic Evidence Map (SEM) Development Workflow [3]

Linking SEMs to Evidence-to-Decision Processes

The EtD process provides a structured framework for moving from evidence to a risk management decision. SEMs integrate into this framework at multiple critical junctures, enhancing its efficiency and scientific rigor.

Informing Problem Formulation and Priority-Setting: Regulatory bodies like the U.S. EPA use SEMs to determine data gaps, identify the need for updated chemical assessments, and set priorities for the agency's assessment portfolio [54]. By mapping the existing evidence, SEMs provide an objective basis for deciding whether a full risk assessment is warranted or if resources should be directed elsewhere.

Streamlining Quantitative Risk Assessment (QRA): In industrial chemical safety, a QRA quantifies the risk of activities involving hazardous substances [99]. An SEM can directly feed into the initial steps of a QRA. The mapped evidence on chemical toxicity, exposure scenarios, and dose-response informs the "hazard identification" and "consequence assessment" phases, making them more comprehensive and less susceptible to bias [99] [100].

Guiding Targeted Evidence Synthesis: The primary output of an SEM is the identification of clusters of evidence suitable for systematic review and glaring evidence gaps requiring primary research [2]. This allows decision-makers to commission precise, high-value SRs to answer the most pressing questions derived from the map, rather than initiating costly SRs on poorly scoped topics.

Table 2: Stage-wise Integration of SEMs into a Quantitative Risk Assessment (QRA) EtD Process [99] [100]

QRA/EtD Stage	Description	Input from SEM
1. Hazard Identification	Identify activities, units, and loss-of-containment scenarios [99].	Evidence on chemical-specific health effects, toxic potencies, and relevant exposure pathways.
2. Consequence Assessment	Model physical effects (e.g., toxic concentration, heat radiation) and damage [99].	Data on dose-response relationships and severity of health outcomes to inform lethality/probit models.
3. Probability Assessment	Assess failure frequencies and conditional probabilities (e.g., ignition, weather) [99].	Context from epidemiological or long-term animal studies may inform base event likelihoods.
4. Risk Calculation	Quantify Individual Risk (IR) and Societal Risk (SR) [99].	Provides the toxicological basis for defining "harm" in risk equations.
5. Risk Evaluation & Decision	Compare risk to acceptance criteria (e.g., ALARP) and decide on measures [99].	Comprehensive evidence overview supports transparent, defensible risk acceptance judgments.

Diagram: Integrating SEM Outputs into the Evidence-to-Decision (EtD) Workflow [2] [54]

Experimental Protocols & Applications

Protocol for a Regulatory SEM (Based on EPA IRIS Template): A definitive protocol for developing an SEM within a regulatory context involves the following detailed steps [3]:

• Objective Definition: Define the map's purpose (e.g., "To characterize the available mammalian and human evidence on the toxicity of Chemical X for priority-setting").
• PECO Statement:
  • Population: Human populations and mammalian animal models.
  • Exposure: Chemical X at any dose, route, or duration.
  • Comparator: Control or comparator groups not exposed to Chemical X.
  • Outcome: All examined health effects, pathologies, and mechanistic endpoints.
• Search Strategy: Develop search strings for databases (e.g., PubMed, Embase, TOXLINE). Include chemical synonyms and registry numbers. Search strategy is peer-reviewed.
• Screening Workflow: Use systematic review software (e.g., DistillerSR, Rayyan). Title/abstract and full-text screening performed by two reviewers independently, with conflicts resolved by consensus or third-party adjudication.
• Data Extraction Fields: Design a standardized form to capture: Reference details, study type (e.g., cohort, chronic bioassay), test system (species, strain), exposure parameters, key outcomes reported, and study funding source.
• Visualization Plan: Plan outputs such as interactive heat maps (health outcome vs. study type) and flow diagrams of evidence distribution.

Case Study: SEM Informing a QRA for an Ammonia Storage Facility:

• SEM Construction: An SEM is conducted on "Ammonia toxicity following acute inhalation." It maps hundreds of studies, identifying clusters of high-quality animal lethality (LC50) studies and human accident reports, but a gap in long-term respiratory morbidity data [3].
• QRA Integration:
  • In Consequence Assessment, the mapped LC50 data from the SEM provides the direct toxicological input for modeling lethal footprints of an ammonia plume [99] [100].
  • In Risk Evaluation, the identified evidence gap on chronic effects is noted as a key uncertainty in the risk characterization section.
• EtD Outcome: The QRA calculates Individual Risk contours. Decision-makers, aware of both the strong acute lethality evidence and the chronic effect uncertainty from the SEM, implement additional risk mitigation measures (e.g., enhanced containment) and recommend specific epidemiological research to address the identified gap [99] [54].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagents, Software, and Methodological Tools for SEM and EtD Integration

Tool Name/Type	Primary Function	Application in SEM/EtD Process
Systematic Review Software (e.g., DistillerSR, Rayyan, CADIMA)	Manages the screening and data extraction process with dual-reviewer workflows and conflict resolution [3].	Essential for conducting the systematic search, screening, and data extraction phases of SEM creation.
PECO Framework	A structured format for defining the key elements of a research question [2].	The foundational step in protocol development for both SEMs and SRs. Defines the scope of evidence gathered.
Machine Learning Classifiers	AI tools trained to prioritize or categorize bibliographic records.	Used in some high-volume SEMs to accelerate initial screening by ranking records by likely relevance [3].
Interactive Visualization Platforms (e.g., Tableau, R Shiny, EPPI-Mapper)	Creates dynamic charts, graphs, and evidence gap maps from extracted data.	Transforms the SEM database into accessible, queryable visualizations for stakeholders and decision-makers [2] [3].
Quantitative Risk Assessment Software (e.g., RISKCURVES, EFFECTS)	Models physical consequences (fire, explosion, dispersion) and calculates individual and societal risk [99].	The primary tool for the EtD stage where SEM-derived toxicological data is applied to calculate quantified risk.
Evidence-to-Decision Framework (e.g., GRADE EtD)	A structured template for transparently documenting judgments on evidence, values, and feasibility.	The formal framework into which SEM outputs (evidence overview, gaps) are fed to structure the deliberation and final decision [54].

Discussion and Future Directions

The formal integration of SEMs into EtD processes addresses long-standing challenges in chemical risk assessment: resource inefficiency, question framing, and evidence surveillance [2] [54]. By providing a scientifically rigorous yet efficient overview, SEMs ensure that subsequent, more resource-intensive steps—whether a full SR, a QRA, or the commissioning of new research—are directed with maximum strategic value.

Future advancements are poised to deepen this integration. The development of living systematic evidence maps, regularly updated with new literature, could provide a perpetual evidence surveillance system for regulatory agencies [2]. Furthermore, the structured data from SEMs are ideal for feeding into computational toxicology and read-across approaches, where machine learning models use mapped data on studied chemicals to predict the toxicity of data-poor substances. Finally, harmonizing SEM templates and outputs across international regulatory bodies, as initiated by the U.S. EPA, promises greater collaboration, data sharing, and consistency in global chemical risk management [3] [54].

Diagram: A Future Vision: Integrated, Data-Driven Risk Assessment Informed by Living SEMs

Conclusion

Systematic Evidence Maps represent a paradigm shift in managing the vast and complex data landscape of chemical risk assessment. By providing a structured, transparent, and queryable overview of existing evidence, SEMs empower researchers and regulators to efficiently identify knowledge gaps, prioritize resources for high-value systematic reviews, and make informed, evidence-based decisions[citation:1][citation:5]. The methodology's strength lies in its flexibility, supporting applications from problem formulation and assessment updates to guiding research agendas[citation:7][citation:8]. Future advancements hinge on the wider adoption of interoperable data structures like knowledge graphs[citation:2], increased integration of AI and automation[citation:10], and the development of standardized reporting guidelines. For biomedical and clinical research, the principles of evidence mapping offer a powerful tool for navigating complex evidence streams in areas like drug safety, mechanistic toxicology, and environmental health, ultimately accelerating the translation of scientific evidence into protective public health policies and safer products.

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