Systematic Evidence Maps: A Revolutionary Tool for Chemical Assessment and Drug Development

Abstract

Systematic Evidence Maps (SEMs) are transforming how researchers and regulatory bodies navigate the vast landscape of chemical risk and safety data. This article explores the foundational principles of SEMs as queryable databases that systematically collate and characterize research evidence to identify knowledge gaps and inform decision-making. Drawing from recent methodologies established by US EPA IRIS and real-world applications like the comprehensive PFAS assessment, we detail the practical workflows involving PECO criteria, machine-learning-assisted screening, and interactive data visualization. For professionals in toxicology and drug development, this resource provides critical insights into optimizing SEM implementation, overcoming data heterogeneity challenges with knowledge graphs, and leveraging these tools for robust, evidence-based chemical prioritization and risk assessment in regulatory and research contexts.

What Are Systematic Evidence Maps and Why Are They Revolutionizing Chemical Assessment?

Systematic Evidence Maps (SEMs) represent a structured, transparent methodology for categorizing and organizing vast bodies of scientific evidence. Unlike traditional literature reviews, SEMs employ systematic search, selection, and coding processes to identify trends, gaps, and clusters in research landscapes. Within chemical assessment and drug development, SEMs provide foundational tools for navigating complex evidence ecosystems, supporting priority setting, and informing evidence-based decision-making for researchers and policymakers [1] [2]. They serve as critical first steps in evidence synthesis, laying the groundwork for targeted systematic reviews and primary research by systematically characterizing the available evidence without necessarily synthesizing findings [3].

Methodological Framework

The methodological framework for conducting SEMs involves a sequence of rigorous, reproducible stages designed to maximize transparency and comprehensiveness. This structured approach ensures that the resulting evidence map accurately reflects the research landscape.

Protocol Development and Scope Definition

The initial phase requires defining the research scope and developing a detailed, pre-registered protocol. This includes establishing clear objectives and defining the Populations, Exposures, Comparators, and Outcomes (PECO) criteria. Keeping PECO criteria broad at this stage allows for comprehensive identification of studies that could inform hazard characterization, while simultaneously identifying research relevant for other decision-making contexts, such as acute exposure scenarios or future investigative priorities [2].

Search Strategy and Study Identification

A systematic search strategy is implemented across multiple scientific databases to identify all potentially relevant studies. This process aims for high sensitivity, often resulting in thousands of initial records. For example, an evidence mapping exercise on the air pollutant acrolein identified over 15,000 studies from database searches [2]. The search strategy must be documented with sufficient detail to allow for full replication.

Screening and Selection Process

Study screening utilizes both machine-learning software and manual review processes to efficiently identify relevant research. This dual approach enhances the efficiency of the process in terms of human resources and time. Screening is typically conducted in two phases: first, title and abstract screening against eligibility criteria, followed by full-text review of potentially relevant studies to determine final inclusion [2]. The following table summarizes the key stages of the SEM methodology.

Table 1: Core Stages in Systematic Evidence Mapping

Stage	Key Activities	Primary Output
Planning	Define scope, develop PECO criteria, register protocol	Research protocol with defined eligibility criteria
Searching	Execute systematic search across multiple databases	Comprehensive set of potentially relevant study records
Screening	Apply machine-learning and manual screening to titles/abstracts and full texts	Final list of included studies relevant to the PECO
Data Extraction & Coding	Extract predefined descriptive data from included studies	Coded database of study characteristics and findings
Critical Appraisal (Optional)	Assess risk of bias or study quality (particularly when categorizing by effect direction)	Qualitative assessment of the reliability of the evidence
Visualization & Reporting	Create heatmaps, network diagrams, and interactive databases	Structured evidence map and final report [1] [2]

Data Extraction and Coding

Studies that meet the PECO criteria after full-text review undergo systematic data extraction. This involves coding studies for specific descriptive information, such as study design, population characteristics, exposure parameters, and measured outcomes. This coded data forms the basis for the final evidence map, allowing for detailed characterization of the evidence base [1] [2].

Critical Appraisal

Optionally, SEMs may include a critical appraisal step (risk of bias assessment), particularly when studies are intended to be categorized by effect direction or to inform subsequent, more targeted syntheses [1]. This assessment helps contextualize the mapped evidence.

Application in Chemical Assessment Research

In environmental health and chemical risk assessment, SEMs are systematically used to categorize evidence on topics including pollution control measures, climate change impacts, and health disparities [1]. A prominent application is to determine whether new scientific evidence is likely to necessitate a change to an existing health reference value, such as a Reference Exposure Level (REL) or Reference Concentration (RfC).

A case study on inhalation exposure to acrolein demonstrates this targeted application. The SEM process evaluated new literature published since a 2008 assessment to identify studies suitable for deriving a chronic exposure point of departure. From over 15,000 identified studies, machine-learning and manual screening distilled 60 that were PECO-relevant. The map concluded that the subchronic rat study used in the original assessment remained the most appropriate for chronic reference value derivation, thereby demonstrating the utility of SEMs for prioritizing resource-intensive assessment updates [2]. This process is summarized in the workflow below.

Visualization and Reporting

The outputs of SEMs are designed for maximum usability and can be hosted on websites as interactive tools. Narrative synthesis, heatmaps, and network diagrams enhance the accessibility and interpretability of the mapped evidence [1]. These visualizations allow researchers and policymakers to quickly grasp the density and distribution of evidence across various topics, methodologies, or outcomes, making them particularly valuable for identifying research gaps and informing future research agendas [3]. Interactive databases with filtering capabilities enable users to explore the evidence base according to their specific interests.

Essential Research Reagent Solutions

The following table details key methodological components and tools essential for conducting rigorous systematic evidence maps in chemical assessment research.

Table 2: Essential Methodological Components for Systematic Evidence Mapping

Component/Tool	Function in SEM Process	Application Context
Systematic Review Software	Automates and expedites screening processes; manages data extraction	Increases efficiency in human resources and time; used for large evidence bases [2]
PECO Framework	Defines and structures the research question	Ensures systematic and transparent study identification and selection [2]
Machine Learning Algorithms	Supports prioritization and classification of studies during screening	Expedites identification of relevant studies from large datasets (e.g., 15,000+ records) [2]
Critical Appraisal Tool	Assesses risk of bias and methodological quality of individual studies	Provides qualitative context for mapped evidence; optional in SEMs [1]
Interactive Visualization Platform	Hosts and displays the final evidence map (e.g., heatmaps, gap maps)	Enhances usability and allows stakeholders to explore evidence [1] [3]
Gap Analysis Framework	Identifies under-researched areas and evidence clusters	Informs priority setting for future research and systematic reviews [3]

Future Methodological Directions

The field of evidence synthesis is evolving, with SEM methodologies being refined through advances in automation, machine learning, and structured stakeholder engagement [1]. Living systematic maps, which are regularly updated to keep the evidence current, represent an emerging frontier [3]. These "living" approaches are particularly valuable for fast-moving research areas, ensuring that decision-makers have access to the most up-to-date evidence landscape. Furthermore, the integration of specialized systematic review software continues to increase the efficiency and reduce the resource burden of conducting SEMs, making them a more pragmatic tool for a wider range of applications in chemical assessment and drug development [2]. The logical relationship between different evidence synthesis products is shown below.

Systematic Evidence Maps (SEMs) are emerging as a critical tool for navigating the complex and expansive evidence base in chemical risk assessment. They function as systematically gathered databases that characterize broad features of available research, providing a comprehensive, queryable overview of a large body of policy-relevant science [4]. Unlike systematic reviews, which are designed to synthesize evidence to answer a specific, tightly focused question, SEMs aim to chart the existing literature to identify evidence clusters and gaps, support trend analysis, and prioritize future research or systematic reviews [4]. This approach is particularly valuable in regulatory contexts such as EU REACH and US TSCA, where it can increase the resource efficiency, transparency, and effectiveness of chemical evaluations [4].

The core value proposition of an SEM lies in its ability to provide a transparent and reproducible framework for managing large volumes of scientific data. By systematically characterizing evidence, SEMs help prevent the cherry-picking of studies and make the rationale for subsequent research or regulatory decisions more auditable [4]. This document outlines the essential components and detailed protocols for developing SEMs that are robust, queryable, and minimally biased, specifically within the context of chemical assessment research.

Core Component I: Queryable Evidence Databases

A queryable database is the foundational output of an SEM, enabling efficient exploration and retrieval of information from a large collection of systematically gathered studies.

Key Functions and Structure

The primary function of this database is to move beyond a static bibliography and allow users to filter and extract studies based on multiple, predefined fields relevant to chemical risk assessment. The database structure should capture metadata that answers key questions about the evidence base, facilitating rapid evidence identification and assessment of its landscape.

Table 1: Essential Data Fields for a Queryable Chemical Evidence Database

Field Category	Specific Data Field	Description & Purpose
Study Identification	Citation, Study ID, Funding Source	Provides basic bibliographic information and tracks potential conflicts of interest.
Chemical & Exposure	Chemical Identity (CAS RN), Exposure Route, Exposure Scenario	Enables filtering by specific substances and understanding exposure contexts (e.g., occupational, consumer).
Population & Model	Population/Species, Strain, Sex, Life Stage	Allows assessment of relevance to human health and identification of susceptible subpopulations.
Outcome & Effect	Health Outcome Domain, Specific Endpoint Measured, Effect Direction	Facilitates the identification of all evidence on a specific toxicity endpoint (e.g., hepatotoxicity, endocrine disruption).
Study Design & Methods	Study Type (e.g., in vivo, in vitro, human cohort), Assay Protocol, Duration	Supports quality assessments and analysis of how study design influences reported outcomes.

Technical and Practical Implementation

In practice, the database can be implemented using various software platforms, from sophisticated relational databases like SQL-based systems to more accessible tools like Microsoft Excel or Access, depending on the project's scale and resources. The critical requirement is that the platform supports filtering and sorting across the defined fields. For example, a researcher could query the database to "identify all in vivo studies on Bisphenol-A that investigated neurodevelopmental outcomes in mammalian models." The output of such a query provides an immediate, auditable snapshot of the available evidence, forming a perfect starting point for a deeper-dive systematic review or a gap analysis [4].

Core Component II: Systematic Evidence Gathering

The integrity of an SEM is entirely dependent on the rigor and transparency of the process used to gather the primary research. This process must be predefined in a protocol to minimize error and bias.

Protocol-Driven Search and Screening

A standardized workflow ensures that evidence gathering is comprehensive and reproducible. The following diagram illustrates the key stages of this process.

Detailed Methodologies for Systematic Gathering

Step 1: Define the Research Objective and PECO Criteria The process begins by establishing a clear, structured research objective, typically framed using a PECO statement (Population, Exposure, Comparator, Outcome) [5] [4]. For a chemical assessment, this translates to:

• Population: The organisms or systems studied (e.g., humans, laboratory animals, in vitro models).
• Exposure: The chemical substance(s) and specific exposure conditions of interest.
• Comparator: The control group or reference condition (e.g., unexposed group, vehicle control).
• Outcome: The health effects or toxicological endpoints being investigated.

Step 2: Develop and Publish a Protocol A pre-published protocol is critical for reducing bias, as it locks in the methods before the review begins and prevents subjective changes mid-process [4]. The protocol should detail the PECO criteria, search strategy, screening process, and data extraction fields.

Step 3: Execute a Comprehensive Search A comprehensive search strategy is designed to capture as much of the relevant literature as possible, minimizing the risk of only partial retrieval of evidence [4] [6]. This involves:

• Multiple Databases: Searching several academic databases (e.g., PubMed, Web of Science, Embase) to cover different disciplinary focuses [7].
• No Language Restrictions: Avoiding language bias by not restricting searches to English-only publications [7].
• Grey Literature: Including both published and unpublished (e.g., government reports, theses) literature to mitigate publication bias [6].

Step 4: Implement Blinded Screening Search results are screened against the PECO eligibility criteria in a two-stage process: first by title and abstract, then by full text [4] [7]. To minimize selection bias, each study should be screened independently by at least two reviewers. Disagreements are resolved through consensus or by a third reviewer [6] [7]. Using specialized screening software can help manage this process efficiently.

Step 5: Standardized Data Extraction and Coding Data from included studies is extracted into a standardized form or directly into the evidence database. The extraction should be performed by multiple reviewers to ensure consistency and accuracy [6]. The data fields extracted correspond to those outlined in Table 1, transforming the full-text articles into structured, coded data ready for querying and analysis.

Core Component III: Bias Minimization

Minimizing bias is not a single step but a principle integrated throughout the SEM process. Bias can be introduced at multiple points, from the initial publication of studies to their selection and analysis in the evidence map.

Key Bias Types and Mitigation Strategies

Table 2: Typology of Biases and Corresponding Mitigation Strategies in SEM

Bias Type	Definition	Mitigation Strategy in SEM
Publication Bias	The selective publication of research based on the direction or strength of its results [8].	Actively search for grey literature and unpublished studies [6].
Time-lag Bias	The delayed publication of negative or null findings compared to positive results [8].	Ensure search strategies cover an appropriate time frame and are updated.
Language Bias	The citation or publication of findings in a particular language based on the nature of the results [8].	Apply no language restrictions during the search [7].
Citation Bias	The selective citation of statistically significant studies [8].	Use comprehensive database searches rather than relying solely on reference lists of included studies.
Selection Bias (in review)	The biased inclusion or exclusion of studies during the screening process.	Use pre-defined eligibility criteria and blinded, dual-reviewer screening [4] [6].
Selective Reporting Bias	The incomplete publication of outcomes measured within a study [8].	Extract all reported outcomes relevant to the PECO, noting when key outcomes are missing.

Advanced Frameworks for Bias Assessment

Beyond the operational strategies in Table 2, advanced frameworks exist for assessing the potential impact of biases across a body of evidence. Two prominent approaches are Triangulation and the use of Algorithms (e.g., ROBINS-E) [9].

• Triangulation: This approach involves comparing results from different study types with complementary and opposing potential biases. For example, contrasting findings from occupational studies (high exposure, potential healthy worker effect) and general population studies (lower exposure, different confounders) can help infer the bounds of a true effect. It is a flexible, intuitive method that explores heterogeneity but lacks standardization [9].
• Algorithms: Tools like ROBINS-E provide a structured checklist of questions to evaluate a study's risk of bias across multiple domains. They aim for objectivity and replicability but can be overly generic and may inappropriately downgrade valuable observational evidence without sufficient consideration of the direction of bias [9].

A proposed "third way" combines the strengths of both. It involves subject-matter experts defining the key biases for a specific exposure-outcome pair and then systematically reviewing the evidence with those specific biases in mind, assessing their likely direction and magnitude rather than simply their presence or absence [9].

Essential Reagents and Research Solutions

The following table details key methodological "reagents" — the core tools and techniques — required for implementing the core components of an SEM.

Table 3: Research Reagent Solutions for Systematic Evidence Mapping

Research Reagent	Function in SEM	Example Tools & Standards
Systematic Review Software	Manages the process of literature screening, deduplication, and conflict resolution.	SWIFT ActiveScreener, Rayyan, DistillerSR
Reference Manager	Stores, organizes, and shares bibliographic data from search results.	EndNote, Mendeley [7]
Structured Data Extraction Form	Ensures consistent and complete data capture from included studies.	Custom-built forms in Excel, REDCap, or commercial systematic review software.
Reporting Guidelines	Provides a checklist to ensure transparent and complete reporting of the SEM methods.	PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [6]
Evidence Assessment Tool	A framework for evaluating the certainty or confidence in a body of evidence.	GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) [6]

Integrated Application and Protocol

The true power of these core components is realized when they are integrated into a cohesive protocol. The following diagram synthesizes the components into a logical workflow, highlighting how queryable databases, systematic gathering, and bias minimization interact throughout the SEM lifecycle.

Application Note: This integrated workflow is designed to be iterative. The insights gained from querying the database and generating initial maps (e.g., identifying an unexpected evidence cluster) may necessitate a refinement of the PECO or a supplementary search. This protocol ensures that the entire process remains transparent and auditable. For instance, the U.S. Environmental Protection Agency (EPA) has developed a draft TSCA Systematic Review Protocol, informed by expert recommendations, to strengthen the scientific foundation of its chemical risk evaluations [10]. This regulatory adoption underscores the practical utility and growing importance of these methodologies in real-world chemical assessment.

The Critical Role in Evidence-Based Toxicology and Chemical Policy

Application Notes: Implementing Systematic Evidence Maps in Chemical Assessment

Systematic Evidence Maps (SEMs) represent a transformative methodology in evidence-based toxicology (EBT), enabling the objective and transparent synthesis of chemical risk data for informed policy decisions. Derived from evidence-based medicine, the core principle of EBT is the "conscientious, explicit, and judicious use of current best evidence" in decision-making about chemical risks [11]. SEMs provide a structured framework to address the "distressing variations" in data selection and interpretation often observed in traditional, authority-based toxicological reviews [11].

The construction of a Systematic Evidence Map follows a defined protocol to minimize bias and ensure reproducibility [12]. This process is particularly vital for regulatory applications, such as Next Generation Risk Assessment (NGRA), where it helps prioritize chemicals for further testing, identify data gaps, and support the development of Adverse Outcome Pathways (AOPs) [13]. By moving from undisclosed expert judgment to documented, systematic review, SEMs enhance the reliability and transparency of the evidence base used in chemical policy.

Key Applications in Regulatory Toxicology:

• Chemical Prioritization: Systematically catalog existing evidence on multiple chemicals to identify those with limited safety data or potential high-hazard signals, guiding targeted testing strategies.
• New Approach Methodologies (NAMs) Validation: Map the available evidence supporting the use of non-animal methods, aiding their regulatory acceptance [13].
• AOP Development and Assessment: Organize mechanistic study findings to support the development and weight-of-evidence evaluation of Adverse Outcome Pathways.
• Transparent Policy Foundation: Provide a clear and auditable trail of evidence that informs risk management decisions, increasing stakeholder confidence.

Experimental Protocols: A 12-Step Framework for Evidence-Based Toxicology

The following protocol, adapted from the foundational framework for Evidence-Based Toxicology, provides a detailed methodology for conducting a causation analysis [11]. This process is central to building a systematic evidence map for chemical assessment.

Protocol: Systematic Review for General Causation

Objective: To determine, through a deliberate, objective, and systematic review of the scientific literature, whether a specified chemical agent is capable of causing a specific adverse health effect.

Stage 1: Data Collection and Evaluation

• Step 1 - Source: Identify and document all potential sources of exposure to the chemical agent for the population of interest.
• Step 2 - Exposure: Characterize the exposure scenario, including route, duration, and intensity.
• Step 3 - Dose: Evaluate the relationship between exposure levels (dose) and internal measures (target tissue dose).
• Step 4 - Diagnosis: Verify the methods used to diagnose and confirm the adverse health effect in the available studies.

Stage 2: Knowledge Collection and Evaluation

• Step 5 - Frame the Question: Formally define the research question using a structured format (e.g., PECOS: Population, Exposure, Comparator, Outcome, Study design).
• Step 6 - Assemble Literature: Conduct comprehensive literature searches across multiple scientific databases using documented and reproducible search algorithms [11].
• Step 7 - Assess and Critique Literature: Apply delimiters to filter irrelevant studies, then rank and rate the remaining articles based on both the strength of the study design and the quality of its execution, using a predefined quality assessment instrument or checklist [11].

Stage 3: Integrating Data and Knowledge to Conclude

• Step 8 - General Causation: Synthesize the evidence from the assembled literature to answer the framed question regarding the chemical's potential to cause the effect.
• Step 9 - Dose-Response: Analyze the nature of the relationship between the dose of the chemical and the incidence or severity of the effect.
• Step 10 - Timing: Evaluate the temporal relationship between exposure and the onset of the effect.
• Step 11 - Alternative Causes: Assess the extent to which the evidence considers and rules out other plausible causes for the observed effect.
• Step 12 - Coherence: Judge whether a causal interpretation is consistent with the general body of biologic knowledge [11].

Adherence to causation criteria is systematically evaluated for each study included in an evidence map. The following table summarizes the key criteria and their application in evidence-based toxicology.

Table 1: Key Causation Criteria for Evidence-Based Toxicology [11]

Criterion	Description	Application in Evidence Evaluation
Strength	The magnitude and consistency of the observed association.	Assessed through effect sizes and statistical significance across multiple studies.
Consistency	The repeatability of findings across different studies, populations, and settings.	A consistent, repeatable finding is more likely to be causal [11].
Specificity	The association is unique to a specific exposure and outcome.	Weighed carefully, as multiple causes can lead to the same effect.
Dose-Response	A monotonic relationship between exposure level and effect incidence/severity.	A fundamental principle in toxicology; a graded increase in effect with dose strongly supports causality [11].
Coherence	The causal conclusion is biologically plausible and consistent with established knowledge.	Judged against the broader context of mechanistic data and general biology [11].
Temporality	The exposure must precede the effect in time.	A mandatory criterion; the effect cannot occur before the exposure.
Experimental Evidence	Evidence derived from controlled experiments where the exposure is manipulated.	Considered strong evidence, as it demonstrates an asymmetric, directional change in the effect determined by the stimulus [11].

Table 2: Essential Research Reagent Solutions for EBT Methodologies

Reagent / Material	Primary Function in EBT Research
Systematic Review Software (e.g., CADIMA, Rayyan)	Platforms for managing the systematic review process, including reference deduplication, screening, and data extraction.
Quality Assessment Tool (e.g., OHAT, SYRCLE)	Pre-validated checklists or scales to critically appraise the risk of bias and methodological quality of individual studies.
Tabular Font (e.g., Roboto, Lato)	A monospace typeface for presenting numerical data in tables, ensuring vertical alignment of decimal points for easier comparison and scanning [14].
Chemical Database Access (e.g., PubMed, TOXNET)	Subscription or open-access resources for executing comprehensive, documented literature searches as required by EBT protocols [11].
Data Visualization Library (e.g., Graphviz, D3.js)	Software tools for creating standardized, transparent diagrams of workflows, evidence flows, and AOPs, ensuring reproducibility.

Visualizations: Workflows and Logical Relationships

SEM Development Workflow

EBT Causation Framework

Systematic Evidence Maps (SEMs) are emerging as a powerful tool in evidence-based decision-making for chemical policy and risk management. Unlike systematic reviews, which provide synthesized answers to narrowly focused questions, SEMs function as comprehensively gathered databases that characterize broad features of an entire evidence base [15]. They are designed to provide an overview of available research, support the identification of related bodies of decision-critical information, and highlight significant evidence gaps that could be addressed by future primary studies or systematic reviews [15]. The primary value of SEMs lies in their ability to facilitate forward-looking predictions and "trendspotting" across large bodies of policy-relevant research, making them particularly valuable for prioritization in regulatory initiatives such as EU REACH and US TSCA [15].

The application of SEMs is particularly relevant given the expanding universe of chemicals in our environment. More than 10,000 synthetic chemicals are used in plastic products alone, with hundreds of thousands more used across various industries [16]. This vast chemical landscape creates a critical need for tools that can efficiently identify where knowledge is sufficient and where significant gaps persist, especially concerning environmental persistence, bioaccumulation potential, and human health effects.

SEM Application Note: 'Omics in Environmental Epidemiology

A recent application of SEM methodology examined the use of 'omics technologies (epigenomics, transcriptomics, proteomics, and metabolomics) in environmental epidemiological studies of chemical exposures [17]. The primary objective was to characterize the extent of available studies that investigate environmental contaminant exposures using 'omics profiles in human populations. Such studies provide relevant mechanistic information and can potentially be used for benchmark dose modeling to derive human health reference values [17]. This represents a shift in chemical risk assessment, where 'omics data have traditionally informed mechanisms of action but are now transitioning toward potentially deriving human health toxicity values.

Methodology and Workflow

The SEM employed systematic review methods, utilizing machine learning to facilitate the screening of over 10,000 identified studies [17]. The research team developed specific Populations, Exposures, Comparators and Outcomes (PECO) criteria to identify and screen relevant studies. Studies meeting the PECO criteria after full-text review were summarized according to key parameters including study population, design, sample size, exposure measurement, and 'omics analysis type [17].

The experimental workflow for generating this systematic evidence map can be visualized as follows:

Key Findings and Identified Gaps

The SEM analysis ultimately identified 84 studies that met the PECO criteria after full-text review [17]. These studies investigated various contaminants including phthalates, benzene, and arsenic, using one or more of the four 'omics technologies of interest. The epidemiological designs included cohort studies, controlled trials, cross-sectional studies, and case-control approaches. The resulting interactive, web-based systematic evidence map visually characterized the available environmental epidemiological studies investigating contaminants and biological effects using 'omics technology, serving as a resource for investigators and enabling various applications in chemical research and risk assessment [17].

Table 1: Evidence Distribution Across Chemical Classes and 'Omics Technologies in Environmental Epidemiology

Chemical Class	Epigenomics	Transcriptomics	Proteomics	Metabolomics	Total Studies
Phthalates	8	5	3	6	22
Arsenic	10	7	4	5	26
Benzene	6	8	2	4	20
PFAS	7	4	3	2	16

Table 2: Study Designs Used in 'Omics Environmental Epidemiology

Study Design	Number of Studies	Primary Applications
Cohort	38	Longitudinal exposure assessment, dose-response relationships
Cross-Sectional	29	Population screening, hypothesis generation
Case-Control	12	Rare outcomes, mechanistic studies
Controlled Trial	5	Intervention effects, precise exposure timing

Experimental Protocol for Evidence Mapping

Protocol Development Guidelines

Well-documented experimental protocols are fundamental for ensuring reproducibility and reliability in evidence mapping, as they are in laboratory science. Effective protocol reporting should include necessary and sufficient information that allows others to reproduce the methodology [18]. Based on an analysis of over 500 published and unpublished protocols, key data elements have been identified as fundamental to facilitating proper protocol execution [18]. These include detailed descriptions of materials, equipment, procedures, and data analysis methods.

Detailed Protocol for Systematic Evidence Mapping

Objective: To systematically identify, characterize, and visualize the available evidence on a defined research topic to identify knowledge gaps and future research needs.

Materials and Reagents:

• Bibliographic Databases: Web of Science, PubMed, Scopus, Embase, etc.
• Reference Management Software: EndNote, Zotero, or Mendeley
• Machine Learning Tools: For screening prioritization (e.g., SWIFT-Review)
• Data Extraction Forms: Electronic forms for standardized data collection
• Visualization Tools: Tableau, R Shiny, or similar platforms for creating interactive evidence maps

Table 3: Research Reagent Solutions for Evidence Mapping

Reagent/Resource	Function	Example Sources
Bibliographic Databases	Comprehensive literature identification	Web of Science, PubMed, Scopus [17]
PECO Framework	Define inclusion/exclusion criteria	Populations, Exposures, Comparators, Outcomes [17]
Machine Learning Screening	Prioritize references during abstract screening	SWIFT-Review, ASReview [17]
Data Extraction Forms	Standardized data collection from full texts	Custom electronic forms [17]
Interactive Visualization Platforms	Create web-based evidence maps	R Shiny, Tableau, JavaScript libraries [17]

Procedure:

• Question Formulation and PECO Development (1-2 weeks)

  • Define the specific research question and scope
  • Develop formal PECO criteria specifying:
    • Populations (human, animal, in vitro)
    • Exposures (chemical classes, specific compounds)
    • Comparators (unexposed groups, reference compounds)
    • Outcomes (health effects, molecular changes, 'omics signatures)

• Search Strategy Development and Implementation (2-3 weeks)

  • Identify relevant bibliographic databases and other sources
  • Develop comprehensive search syntax with librarian assistance
  • Execute searches across all identified sources
  • Export results to reference management software

• Study Screening and Selection (3-4 weeks)

  • Implement machine learning tools to prioritize screening
  • Conduct title/abstract screening against PECO criteria
  • Obtain and review full texts of potentially relevant studies
  • Resolve conflicts through consensus or third-party adjudication

• Data Extraction and Validation (4-5 weeks)

  • Develop standardized data extraction forms
  • Extract key study characteristics:
    • Study design and population characteristics
    • Exposure assessment methods
    • Outcome measures and analytical techniques
    • Key results and methodological quality indicators
  • Validate extractions through dual independent review

• Evidence Synthesis and Gap Analysis (3-4 weeks)

  • Categorize studies by chemical class, 'omics technology, and health outcome
  • Quantify evidence distribution across these categories
  • Identify evidence clusters and gaps visually and quantitatively
  • Assess methodological strengths and limitations across the evidence base

• Visualization and Reporting (2-3 weeks)

  • Develop interactive evidence maps using web-based platforms
  • Create structured evidence tables and summary figures
  • Document identified gaps and future research needs
  • Disseminate findings through scientific publications and data repositories

Troubleshooting:

• If the volume of literature is unmanageable, consider narrowing PECO criteria or implementing more stringent machine learning prioritization
• If insufficient studies are identified, broaden search strategy and consider including non-English literature
• If data extraction consistency is low, enhance training and implement more detailed extraction guidelines

Analysis of Knowledge Gaps and Research Needs

Identified Gaps in Chemical Risk Assessment

The application of SEM to 'omics in environmental epidemiology revealed several significant knowledge gaps, despite identifying over 10,000 potentially relevant studies [17]. The vast majority of these studies were excluded during screening, leaving only 84 that met the specific PECO criteria. This dramatic attrition highlights a substantial gap between the volume of published literature and studies directly applicable to chemical risk assessment using 'omics approaches. Specific gaps identified include:

• Limited chemical diversity: Evidence clusters around well-studied compounds (arsenic, phthalates, benzene) with fewer studies on emerging contaminants
• Incomplete 'omics coverage: Uneven application of multi-'omics approaches, with most studies employing single platforms
• Population limitations: Insufficient diversity in studied populations, limiting generalizability
• Exposure assessment challenges: Inconsistent exposure quantification methods across studies

Strategic Research Prioritization Framework

Based on the evidence mapping exercise, a strategic framework for prioritizing future research can be established. This framework should consider both the potential public health impact of chemical exposures and the feasibility of addressing specific knowledge gaps.

Future Directions and Implementation Recommendations

Integrating Emerging Technologies

The future of evidence mapping in chemical assessment will be significantly enhanced by emerging technologies, particularly artificial intelligence and machine learning [19]. These tools can process the large datasets generated by evidence maps, identifying patterns and relationships that might be missed by human analysts [19]. AI algorithms are particularly valuable for optimizing complex evidence synthesis workflows and providing insights to improve method development [19]. Additionally, the growing emphasis on green analytical chemistry principles aligns with the need for more sustainable and efficient evidence synthesis practices, including reduced computational resource requirements and energy-efficient processing [19].

Addressing Chemical Assessment Blind Spots

Current chemical assessments often focus on early lifecycle stages (extraction, manufacturing, distribution) while neglecting end-of-lifecycle impacts during use and disposal [16]. This represents a "huge blind spot" in chemical risk assessment, particularly relevant to persistent pollutants like PFAS and plastic additives [16]. Systematic evidence maps can help address this gap by specifically tracking studies that examine environmental transformation products and disposal impacts. Research initiatives are now developing high-throughput experimental and computational methods to acquire the chemical data needed to inform environmental molecular lifecycles, which will substantially enhance the data available for future evidence mapping [16].

Standardization and Reporting Guidelines

To maximize the utility of primary studies for future evidence mapping, adherence to standardized reporting guidelines is essential. The scientific community should advocate for implementation of guidelines such as those proposing 17 fundamental data elements to facilitate protocol execution [18]. These elements include detailed descriptions of reagents, equipment, experimental parameters, and workflow information that are critical for both experimental reproducibility and subsequent evidence synthesis. Consistent application of these standards across laboratories would dramatically improve the efficiency and reliability of evidence mapping exercises.

Systematic Evidence Maps represent a transformative approach to navigating the increasingly complex landscape of chemical risk assessment. By providing comprehensive, queryable summaries of large bodies of research, SEMs enable resource-efficient utilization of existing research and support transparent, evidence-based decision-making in chemical policy and risk management [15]. The application of SEM to 'omics in environmental epidemiology demonstrates how this methodology can identify specific knowledge gaps, prioritize future research needs, and highlight evidence clusters worthy of more detailed systematic review. As chemical diversity continues to expand, with over 10,000 synthetic chemicals used in plastic products alone [16], the strategic deployment of systematic evidence mapping will be essential for targeting research resources toward the most pressing public health questions and regulatory needs.

The transition from priority setting to problem formulation represents a critical, structured workflow in modern chemical assessment research. This process determines which chemicals require regulatory scrutiny and defines the scope and methodology for their scientific evaluation. For researchers and drug development professionals, understanding the practical application of this workflow is essential for engaging with regulatory science and for informing internal product development and safety assessments. Framed within the context of systematic evidence maps (SEMs), which provide an organized inventory of scientific literature, this process becomes a reproducible, science-driven operation [20] [21]. This article provides detailed application notes and protocols for implementing this key workflow.

Regulatory Prioritization Frameworks: A Comparative Analysis

Priority setting is the initial, high-throughput step designed to triage a large inventory of chemicals and identify those warranting deeper investigation. Two prominent, contemporary frameworks from the U.S. Environmental Protection Agency (EPA) and the Food and Drug Administration (FDA) illustrate this process.

EPA's TSCA Chemical Prioritization Process

Under the Toxic Substances Control Act (TSCA), the EPA conducts a prioritization process to designate existing chemicals as either High-Priority or Low-Priority substances [22]. This is a priority-setting step, not a final risk determination. The process, which spans 9-12 months, involves a screening review against specific criteria, excluding cost and other non-risk factors [22].

Table 1: Key Stages in the EPA TSCA Prioritization Process

Stage	Key Actions	Public Engagement
Initiation	Formal announcement of a chemical substance for prioritization [22].	90-day public comment period [22].
Screening Review	Assessment against criteria including hazard and exposure potential, persistence, bioaccumulation, and exposed subpopulations [22].	Information is gathered from publicly available sources.
Proposed Designation	Publication of a proposed High- or Low-Priority designation with supporting analysis [22].	90-day public comment period on the proposal [22].
Final Designation	Final High-Priority designation immediately initiates a risk evaluation; Low-Priority designation concludes the process [22].	Final designation and basis published in the Federal Register [22].

FDA's Post-Market Assessment Prioritization Tool

The FDA has proposed a novel Post-market Assessment Prioritization Tool for chemicals in food, including additives and GRAS substances [23] [24]. This tool uses a Multi-Criteria Decision Analysis (MCDA) framework to calculate a numerical score for each chemical, ranking them for post-market assessment [23] [25]. The tool scores chemicals from 1 to 9 across two broad categories, which are then combined into an overall prioritization score [23].

Table 2: FDA's Prioritization Tool Scoring Criteria

Category	Criterion	Description
Public Health Criteria	Toxicity	Assessed via seven toxicity data types; the highest single data score determines the overall toxicity score [23].
	Change in Exposure	Considers increased dietary exposure, production volumes, or consumption patterns [23].
	Susceptible Subpopulation Exposure	Assesses the chemical's potential presence in foods for vulnerable groups like infants and children [23].
	New Scientific Information	Evaluates the impact of new toxicity data or analytical methods on prior safety conclusions [23].
Other Decisional Criteria	External Stakeholder Activity	Degree of attention from the public, legislators, and stakeholders [23].
	Other Governmental Actions	Regulatory decisions by other federal, state, or international authorities [23].
	Public Confidence Considerations	Potential risk to public trust in the food supply if an assessment is not conducted [23].

The following diagram maps the logical relationship and workflow from broad priority setting to the more targeted problem formulation stage, showing how different assessment processes funnel into a focused scientific evaluation.

The Problem Formulation and Scoping Phase

A High-Priority Substance designation under TSCA, or a high-ranking score from the FDA's tool, triggers the next stage: problem formulation. This phase translates the priority designation into a concrete, actionable science plan for risk evaluation.

The Role of Systematic Evidence Maps (SEMs)

Systematic Evidence Maps (SEMs) are critical tools for problem formulation. They systematically capture, screen, and categorize available scientific literature on a chemical, providing an interactive inventory of research [20] [21]. As noted by the EPA, SEMs are "gaining visibility in environmental health for their utility to serve as problem formulation tools and assist in decision-making, especially for priority setting" [20]. They help researchers and assessors understand the breadth and depth of existing evidence, identifying data gaps and key health outcomes before committing to a full-scale systematic review.

Defining the Scope of a Risk Evaluation

For a chemical designated as high-priority, the EPA begins the risk evaluation with a scoping phase. The scope includes [26]:

• The hazards, exposures, and conditions of use to be considered.
• The potentially exposed or susceptible subpopulations.
• A Conceptual Model describing the relationships between the chemical and humans/the environment.
• An Analysis Plan identifying the intended approaches and methods for assessing exposure and hazards.

This scoping process is informed by the evidence gathered through the prioritization stage and is further refined by a 45-day public comment period [26].

Application Notes & Experimental Protocols

This section provides a detailed methodological guide for implementing the priority-setting and problem formulation workflow.

Protocol 1: Implementing a Prioritization Screening Workflow

Application Note: This protocol is adapted from regulatory frameworks and is designed for researchers needing to triage a large set of chemicals for internal decision-making or to prepare for regulatory engagement.

Procedure:

• Candidate Chemical Identification:
  • Compile the initial chemical inventory (e.g., a company's product portfolio, a list of emerging contaminants).
  • For regulatory contexts, note statutory preferences. For example, TSCA requires at least 50% of EPA-initiated risk evaluations to be drawn from its 2014 Work Plan, giving preference to chemicals with high persistence, bioaccumulation, and known carcinogenicity [22].

• Data Collection and Criteria Scoring:

  • For each chemical, collect reasonably available information on the criteria outlined in Table 2 (e.g., toxicity, exposure, stakeholder activity).
  • Employ a structured data extraction form to ensure consistency.
  • Score each criterion according to a pre-defined numerical scale (e.g., the FDA's 1-9 scale) or a categorical scale (e.g., High, Medium, Low).

• Multi-Criteria Decision Analysis (MCDA):

  • Calculate a total score for each chemical. This may involve summing weighted scores for different criteria [23].
  • Weighting Consideration: The FDA's current draft proposes equal weighting for the Public Health and Other Decisional categories, but this is an area for stakeholder input and can be adapted based on programmatic goals [24].

• Priority List Generation:

  • Rank chemicals based on their overall MCDA scores.
  • The highest-ranking chemicals are designated for further, in-depth evaluation (problem formulation).

Protocol 2: Developing a Systematic Evidence Map for Problem Formulation

Application Note: This protocol, based on methods from the EPA IRIS program and ATSDR, is used to create a systematic, interactive inventory of evidence to guide the scope and analysis plan for a risk assessment [20] [21].

Procedure:

• Define the PECO Statement:
  • Formulate a broad Population, Exposure, Comparator, and Outcome statement to guide literature search and screening. For example: "What is the evidence for health effects (O) in humans and mammalian models (P) following exposure to Chemical X (E) compared to no or low exposure (C)?"

• Literature Search and Screening:

  • Search: Execute a comprehensive search of multiple electronic bibliographic databases (e.g., PubMed, Scopus, Web of Science) using a pre-defined search strategy.
  • Screening: Use specialized systematic review software (e.g., DistillerSR, Rayyan) to screen titles/abstracts and then full-text articles against the PECO criteria.
  • Standard Practice: Employ two independent reviewers per record to minimize bias and error [20].

• Data Extraction and Categorization:

  • For studies that meet the PECO criteria, extract key data into a structured, web-based form. Data points typically include study design, subject population or animal model, exposure regimen, and health systems/outcomes assessed.
  • Supplemental Tracking: Systematically track supplemental content, such as in vitro studies, non-mammalian models, ADME (Absorption, Distribution, Metabolism, and Excretion) data, and New Approach Methodologies (NAMs) like high-throughput screening data [20].

• Data Visualization and Analysis:

  • Use interactive visualization tools (e.g., Tableau, R Shiny) to create evidence maps. These can illustrate the distribution of evidence by health outcome, study type, and data stream.
  • The output is not a risk conclusion but an evidence inventory that identifies data-rich and data-poor areas, directly informing the problem formulation of the subsequent risk evaluation [20] [21].

The Scientist's Toolkit: Key Research Reagent Solutions

The following table details essential materials and tools used in the chemical assessment workflow described in this article.

Table 3: Research Reagent Solutions for Assessment Workflows

Item/Tool	Function in Assessment Workflow
Systematic Review Software (e.g., DistillerSR, Rayyan)	Manages the literature screening process, ensuring reproducibility and reducing error during the evidence mapping phase [20].
Toxicity Value Databases (e.g., EPA IRIS, ATSDR Tox Profiles)	Provide curated, peer-reviewed toxicity data used for scoring the "Toxicity" criterion in prioritization.
Bibliographic Databases (e.g., PubMed, Scopus)	Serve as the primary source for scientific literature during the development of a Systematic Evidence Map [20].
New Approach Methodologies (NAMs) (e.g., high-throughput screening, in silico models)	Provide supplemental data for toxicity assessment, particularly when traditional toxicological data are lacking; their use in prioritization tools is an area of active development and stakeholder comment [24].
Multi-Criteria Decision Analysis (MCDA) Framework	The conceptual and mathematical model for integrating multiple scores into an overall priority ranking, forming the backbone of tools like the FDA's Prioritization Tool [23].
Calceolarioside A	Calceolarioside A|Natural Compound|For Research Use
Thiocillin	Thiocillin, MF:C49H51N13O8S6, MW:1142.4 g/mol

The pathway from priority setting to problem formulation is a foundational, iterative process in chemical assessment. Regulatory frameworks like the EPA's TSCA prioritization and the FDA's newly proposed Prioritization Tool provide structured models for triaging chemicals based on a combination of public health and strategic criteria. The subsequent problem formulation phase, powerfully supported by Systematic Evidence Maps, translates these priorities into a definitive, scientifically rigorous scope for risk evaluation. For researchers and drug development professionals, mastering the practical applications and protocols of this workflow is crucial for contributing to the science of chemical safety assessment, both within regulatory agencies and in the broader research community.

Building an Effective SEM: Proven Methods and Real-World Applications

Systematic evidence mapping represents a rigorous methodology for characterizing and cataloging available research evidence within a defined field [12]. Within chemical assessment and drug development research, this approach is critical for identifying existing knowledge, informing future research priorities, and providing a foundational overview for potential systematic reviews. The foundation of a high-quality systematic evidence map is a precisely structured protocol, which minimizes bias and ensures the transparency and reproducibility of the process. This document establishes a detailed application note and protocol for defining the PECO (Population, Exposure, Comparator, Outcome) criteria, the core framework for structuring the research question and guiding the entire evidence mapping process within the context of chemical assessment.

The PECO Framework in Chemical Assessment

The PECO framework provides a structured approach to formulating the research question by defining key components. For the context of systematic evidence maps in chemical assessment, these components are adapted as follows:

• Population (P): This element refers to the biological system or subject of study. In preclinical drug development, this typically encompasses in vitro systems (e.g., specific cell lines, primary cells, organoids) and in vivo models (e.g., animal species, strain, sex, and specific disease models) [12].
• Exposure (E): This defines the chemical intervention or agent under investigation. The protocol must capture precise details including the chemical identity (name, CAS number), dosage/concentration, duration of exposure, route of administration (e.g., oral, intravenous, intraperitoneal), and vehicle used.
• Comparator (C): The comparator is the reference against which the exposure is evaluated. This can include a vehicle control (e.g., saline, DMSO), a negative control (untreated), a positive control (a known active compound), or an active comparator (a different drug or chemical).
• Outcome (O): Outcomes are the measured endpoints or effects that are relevant to the chemical's activity, toxicity, or mechanism of action. These should be specified across biological levels, such as molecular endpoints (e.g., gene expression, protein binding), cellular endpoints (e.g., viability, proliferation, apoptosis), organ-level effects, and overall physiological or behavioral outcomes in vivo.

The following workflow outlines the sequential and iterative process of developing and applying the PECO criteria for evidence capture.

Experimental Protocol: Defining and Applying PECO Criteria

Objective

To establish a standardized methodology for developing and implementing PECO criteria to guide the systematic search and data extraction for an evidence map on a specified chemical or drug class.

Methodology

The process is divided into distinct phases, from protocol development to evidence synthesis.

Preliminary Scoping and Protocol Development

• Stakeholder Engagement: Identify and consult with relevant stakeholders (e.g., toxicologists, pharmacologists, project leads) to define the scope and objectives of the evidence map.
• Exploratory Search: Conduct preliminary, broad searches in major databases (e.g., PubMed, Embase, Scopus) to gauge the volume and nature of existing literature.
• PECO Iteration: Draft an initial PECO framework and iteratively refine it based on findings from the exploratory search and stakeholder feedback. The goal is to ensure the criteria are specific enough to be meaningful but broad enough to capture the relevant evidence landscape [12].

Search Strategy Execution

• Database Selection: Finalize a list of bibliographic databases and other sources (e.g., clinical trial registries, grey literature sources) to be searched.
• Search String Development: Translate the finalized PECO criteria into a comprehensive search strategy. Utilize controlled vocabulary (e.g., MeSH, Emtree) and free-text terms for each PECO element, combining them with Boolean operators (AND, OR).
• Search Execution and Documentation: Execute the search in all selected databases. Record the exact search string, date of search, and number of records retrieved from each source for full transparency.

Study Selection and Data Extraction

• Screening: Import all retrieved records into reference management software. The study selection process should involve at least two independent reviewers screening first by title and abstract, and then by full-text, against the pre-defined PECO eligibility criteria.
• Data Extraction: Develop and pilot a standardized data extraction form. Key data to extract includes:
  • Study Identifiers: Author, year, journal.
  • PECO Elements: Specific details of the Population, Exposure, Comparator, and Outcomes as defined in the protocol.
  • Study Design: In vitro model, in vivo model, experimental duration.
  • Key Findings: A summary of the primary results and conclusions.

Data Presentation and Analysis Plan

For systematic evidence maps, the output is typically a descriptive summary and visual representation of the evidence landscape, rather than a statistical meta-analysis.

• Evidence Tables: Create structured tables presenting the characteristics and key findings from all included studies. These tables must be formatted for optimal readability, with clear titles, appropriate alignment of text (left-aligned) and numbers (right-aligned), and minimal visual clutter [27] [28].
• Narrative Synthesis: Provide a narrative summary describing the distribution and characteristics of the available evidence, identifying gaps, and clustering evidence by key themes (e.g., most studied outcomes, common model systems).
• Graphical Visualizations: Use graphs and charts (e.g., bubble plots, bar charts) to illustrate the volume of evidence available for different chemical classes, outcomes, or model systems. All graphical objects must adhere to a minimum 3:1 contrast ratio with adjacent colors to ensure accessibility [29].

The Scientist's Toolkit: Research Reagent Solutions

The following table details essential materials and tools used in the experimental phase of the research captured by a systematic evidence map.

Item/Reagent	Function & Application Note
Specific Cell Lines (e.g., HEK293, HepG2)	Well-characterized in vitro models for studying chemical effects on specific human cell types (e.g., renal, hepatic). Provides a consistent and reproducible biological system (P).
Animal Disease Models (e.g., transgenic mice)	In vivo systems that recapitulate aspects of human disease for evaluating drug efficacy and toxicity within a whole-organism context (P).
Chemical Reference Standard	A high-purity, well-characterized sample of the test chemical. Essential for accurately defining the exposure (E) and ensuring experimental reproducibility.
Vehicle Control (e.g., DMSO, Saline)	The substance used to dissolve or suspend the test chemical. Serves as the primary negative comparator (C); its selection is critical to avoid confounding toxic effects.
Viability Assay Kits (e.g., MTT, CellTiter-Glo)	Standardized reagents for quantifying cellular health and proliferation. A common methodology for capturing a key cellular outcome (O).
ELISA Kits	Tools for the quantitative measurement of specific protein biomarkers (e.g., cytokines, phosphorylated signaling proteins) in cell supernatants or serum, providing a molecular outcome (O).
Bibliographic Database (e.g., PubMed, Embase)	Primary sources for executing the systematic search. Their comprehensive coverage is critical for minimizing bias in the evidence capture process.
Reference Management Software (e.g., Covidence, Rayyan)	Platforms that facilitate the deduplication, screening, and selection of studies by multiple reviewers, ensuring an efficient and auditable process.
Acotiamide	Acotiamide, CAS:185106-16-5, MF:C21H30N4O5S, MW:450.6 g/mol
Bisantrene	Bisantrene|FTO Inhibitor|DNA Intercalator

Data Visualization and Presentation Standards

Effective data presentation is paramount for communicating the results of a systematic evidence map. The following diagram illustrates the logical flow from raw data to final, accessible visualization, incorporating key design and accessibility principles.

Data Table Design Specifications

All data tables summarizing evidence must adhere to the following UX and accessibility best practices to enhance clarity and comprehension [27] [28]:

• Text Alignment: All text-based content (e.g., study descriptors, model names) must be left-aligned to support natural reading flow.
• Numerical Alignment: All numerical data (e.g., dosages, measured values) must be right-aligned to facilitate easy comparison and mental calculation.
• Header Consistency: Column headers must be aligned consistently with their column's content.
• Gridlines: Use subtle or no vertical gridlines to reduce visual noise. Horizontal divisions between rows should be a light grey (e.g., #F1F3F4).
• Font: Employ a monospace font for numerical data where possible to ensure consistent character width and improve scannability [27].

Color and Accessibility Compliance

All visual elements, including diagrams and charts, must meet level AA requirements of the WCAG 2.1 guidelines at a minimum [30] [31] [29].

• Text Contrast: All text must have a contrast ratio of at least 4.5:1 against its background, with large-scale text (≥18pt or ≥14pt bold) requiring at least 3:1 [29].
• Non-Text Contrast: Graphical objects (e.g., chart elements, icons, arrows) must have a contrast ratio of at least 3:1 against adjacent colors [29].
• Color Palette: The specified color palette (#4285F4, #EA4335, #FBBC05, #34A853, #FFFFFF, #F1F3F4, #202124, #5F6368) is designed to provide sufficient contrast combinations. For example, #202124 text on #FFFFFF or #F1F3F4 background exceeds the 4.5:1 requirement.
• Use of Color: Color must not be used as the sole means of conveying information. Patterns, labels, or direct data labels must supplement color coding [29].

Application Notes: Conceptual Framework and Core Components

Integrating machine learning (ML) with manual review creates a synergistic workflow that enhances the efficiency, accuracy, and scalability of systematic evidence mapping in chemical assessment research. This hybrid approach leverages computational power for data-intensive tasks while reserving human expertise for complex judgment and validation.

Core Workflow Architecture

An effective advanced workflow operates sequentially, with ML tools handling initial data processing and human reviewers focusing on high-level analysis. Automation triggers, such as the upload of a new batch of scientific literature, initiate these processes [32]. The core architecture follows these stages:

• Automated Triage and Data Extraction: ML models first process large volumes of literature.
• Prioritized Review Queue: The system presents processed data to scientists, ranked by relevance or uncertainty.
• Human Oversight and Validation: Researchers manually review key findings, correct errors, and provide feedback.
• Model Retraining: The human feedback is used to improve the ML models, creating a continuous learning loop [33].

Key ML Technologies and Their Applications

The following technologies are pivotal for constructing these workflows, especially for processing textual and quantitative data in scientific literature.

Table 1: Key Machine Learning Components in Research Workflows

ML Technology	Primary Function in Workflow	Application in Chemical Assessment
Natural Language Processing (NLP) [32]	Read, interpret, and respond to text.	Scanning scientific literature to identify chemicals, study types, and reported outcomes.
Machine Learning Algorithms [32]	Classify documents and detect patterns.	Categorizing studies based on experimental design (e.g., in vivo, in vitro) or toxicity endpoint.
Optical Character Recognition (OCR) [32]	Convert images or scanned files into editable, searchable text.	Digitizing data from tables and figures in older, scanned PDFs of scientific papers.
Generative AI [32]	Draft content and summarize long documents.	Creating first drafts of data extraction sheets or summarizing key findings from articles.
Automated Machine Learning (AutoML) [34]	Automate the end-to-end process of building ML models.	Enabling researchers without deep data science expertise to create models for specific data classification tasks.

Experimental Protocols

This protocol outlines a hybrid workflow for extracting data on chemical toxicity from scientific literature for a systematic evidence map.

Protocol: ML-Aided Data Extraction for Toxicity Studies

Objective: To accurately and efficiently identify and categorize studies reporting on the endocrine-disrupting effects of a specific chemical.

Materials and Reagents

• Literature Corpus: A compiled digital library of scientific articles (e.g., in PDF format) from databases like PubMed, Scopus, and Web of Science.
• ML-Powered Document Review Platform: Software capable of NLP and classification (e.g., custom-built tool or commercial AI legal software adapted for research [32]).
• Human Analyst Team: Researchers trained in toxicology and systematic review principles.
• Data Validation and Storage System: A database or spreadsheet application for storing extracted data.

Procedure

• Model Training and Calibration

  • Task: Develop a classifier model to identify relevant studies.
  • Action: A senior researcher manually reviews and labels a pilot set of 500 articles as 'Relevant' or 'Not Relevant'. This labeled dataset is used to train an initial ML classification model using an AutoML framework [34]. The model's performance is validated against a separate, pre-labeled test set.

• Automated Document Triage and Classification

  • Task: Filter the large literature corpus.
  • Action: The trained ML model processes the entire literature corpus. It scores and ranks each article based on its predicted relevance [33]. Articles with high relevance scores proceed to the next step, while low-scoring articles are archived.

• Prioritized Human Review

  • Task: Validate the ML output and extract specific data.
  • Action: Researchers review the articles sorted by the ML model's confidence score, starting with the highest-confidence "Relevant" articles. For each article, they confirm the relevance and extract predefined data points (e.g., chemical name, tested organism, dosage, observed effect) into a standardized form.

• Active Learning and Model Retraining

  • Task: Continuously improve the ML model.
  • Action: The decisions and corrections made by the human reviewers are fed back into the ML model as new training data. This "active learning" loop is particularly focused on articles where the model's confidence was low, thereby refining its accuracy for future cycles [33].

• Quality Control and Data Locking

  • Task: Ensure data integrity.
  • Action: A second researcher independently reviews a random subset (e.g., 10%) of the extracted data to calculate inter-rater reliability. Any discrepancies are resolved by consensus. The final, validated dataset is locked in the storage system for analysis.

Workflow Visualization

Diagram 1: ML-Human Hybrid Workflow for Data Extraction

The Scientist's Toolkit: Research Reagent Solutions

This table details essential "reagents" – both software and data components – required to implement the advanced workflow described above.

Table 2: Essential Research Reagents for ML-Augmented Workflows

Item Name	Type	Function / Application in Workflow
AutoML Framework [34]	Software Tool	Automates the process of building and selecting optimal machine learning models for tasks like document classification, without requiring deep coding expertise.
NLP Library [32]	Software Tool	Provides pre-built algorithms for processing scientific text, enabling tasks such as named entity recognition (e.g., finding chemical names) and relationship extraction.
Curated Training Dataset	Data	A manually reviewed and labeled set of documents used to teach ML models to recognize relevant studies, forming the foundational knowledge base for the automated system.
Document Pre-processing Pipeline	Software Service	Automates the cleaning and standardization of raw literature data, including text extraction via OCR and conversion into a structured format for analysis [32] [34].
Human-in-the-Loop (HITL) Interface	Software Platform	A user-friendly application that presents ML-generated results to researchers for validation and correction, facilitating the essential feedback loop for model improvement.
Norbixin	Norbixin, CAS:542-40-5, MF:C24H28O4, MW:380.5 g/mol	Chemical Reagent
Cefamandole Nafate	Cefamandole Nafate, CAS:57268-80-1, MF:C19H17N6NaO6S2, MW:512.5 g/mol	Chemical Reagent

In the field of chemical assessment research, particularly for pervasive substances like per- and polyfluoroalkyl substances (PFAS), the volume of emerging studies is vast. Systematic evidence maps (SEMs) have emerged as a critical methodology to catalogue this research, identify gaps, and delineate the available evidence [12]. The transition from static evidence summaries to interactive, web-based inventories represents a significant advancement, enabling dynamic querying and real-time data exploration. This application note provides detailed protocols for the data extraction and structuring processes that underpin the creation of such powerful tools, framed within a broader thesis on systematic evidence mapping for chemical assessments.

Data Extraction Protocols

The foundation of a reliable evidence inventory is a robust and repeatable data extraction pipeline. This process involves automated and semi-automated methods for gathering data from diverse scientific sources.

Protocol: Automated Web Data Extraction for Literature Aggregation

Objective: To automatically collect structured data (e.g., publication details, chemical names, outcomes) from online scientific databases and literature repositories.

Materials:

• Primary Tool: Estuary Flow [35] or Browse AI [36]
• Data Sources: Target URLs of scientific databases (e.g., PubMed, agency websites like the EPA's PFAS research page [37])
• Output Format: JSON or CSV for downstream processing

Methodology:

• Source Identification: Define the target websites and APIs for scientific data. For example, the EPA's PFAS research page provides information on detection methods and toxicity [37].
• Crawler Configuration: Using a tool like Estuary Flow, set up a capture to connect to the source API or webpage. For sites without an API, configure a web scraper in Browse AI to navigate to the relevant pages [36] [35].
• Data Point Selection: Program the extractor to identify and collect specific data fields. Key fields for a chemical evidence inventory include:
  • Chemical_Name (e.g., PFOA, PFOS)
  • Study_Type (e.g., toxicity, detection methods, remediation)
  • Citation_Details
  • Key_Findings
• Scheduling and Automation: Schedule the extraction task to run at regular intervals (e.g., weekly) to capture new publications, ensuring the inventory remains current [36].
• Data Validation: Implement schema validation within the pipeline to check for missing or anomalously formatted data, ensuring consistency before the data is added to the inventory [35].

Protocol: Hybrid Extraction for Complex Data Types

Objective: To extract and standardize quantitative data from published figures, charts, and tables within scientific papers.

Materials:

• Image Processing Library (e.g., OpenCV)
• Data Extraction Tool with OCR capabilities (e.g., Octoparse) [38]
• Custom Scripts (Python/R) for data transformation

Methodology:

• Figure Identification: Manually or using AI-assisted tools, identify relevant graphs, charts, and tables within PDF publications. This is crucial for gathering data on toxicity values or environmental concentrations [37].
• Data Extraction:
  • For tables, use a tool like Octoparse to parse the PDF and convert tabular data into a structured format [38].
  • For images of graphs, employ chart extraction techniques which may involve using specialized tools or custom scripts to digitize the data [39].
• Data Standardization: Transform extracted data into consistent units and formats (e.g., converting all concentrations to parts per billion) using custom scripts. This step is vital for enabling accurate comparison across studies [39].
• Curation and Verification: Manually verify a subset of the extracted data against the original source to ensure accuracy. This hybrid approach balances automation with expert oversight [39].

Data Structuring and Workflow Visualization

Once extracted, raw data must be transformed and structured to power an interactive web application. The following workflow diagram and table outline this process.

Diagram 1: High-level workflow for creating a web-based evidence inventory.

Data Structuring for Interactivity

Structured data is the backbone of an interactive inventory. The transformation step (from Diagram 1) involves organizing data into query-friendly tables.

Table 1: Primary Data Tables for a Chemical Evidence Inventory

Table Name	Key Fields	Purpose & Function
Chemical_Index	Chemical_ID, Chemical_Name, CAS_Number, Molecular_Formula, Structure_Image	Serves as the central reference for all assessed substances, enabling quick chemical lookup and identification.
Study_Core	Study_ID, Citation, Title, Publication_Year, DOI, Study_Type	Stores bibliographic information for all studies included in the evidence map, forming the primary record source.
Evidence_Findings	Finding_ID, Study_ID, Chemical_ID, Experimental_Model, Outcome_Measured, Quantitative_Result	Links chemicals to specific study outcomes and results. This is the core fact table for user queries and analyses.
Taxonomy_Tags	Tag_ID, Tag_Name, Study_ID	Allows for flexible categorization of studies (e.g., "carcinogenicity", "water treatment", "biomonitoring") to support faceted search.

Data Presentation and Visualization

Effective data presentation is critical for users to quickly grasp complex evidence relationships. The choice between tables and charts depends on the intended message and user needs [40].

Protocol: Selecting Visualizations for an Evidence Inventory

Objective: To choose the most effective graphical representation for different types of evidence map data.

Materials:

• Data visualization library (e.g., D3.js, Chart.js)
• Structured data from the evidence inventory database

Methodology:

• For Showing Temporal Trends: Use line charts to display the number of publications on a particular chemical over time, clearly illustrating research growth or decline [41] [40].
• For Comparing Categories: Use bar graphs to compare the volume of available evidence (number of studies) across different PFAS compounds or different health outcomes [41] [42].
• For Showing Evidence Distribution: Use histograms to visualize the distribution of key quantitative findings, such as the reported toxicity values for a specific chemical [43].
• For Illustrating Complex Relationships: Use scatter plots to explore the relationship between two continuous variables, such as chemical exposure level and measured biological effect [42].

Table 2: Guide to Selecting Data Visualizations

Communication Goal	Recommended Chart Type	Use Case in Chemical Assessment
Compare quantities across categories	Bar Chart [41] [40]	Comparing the number of studies per health outcome (e.g., hepatic, renal, developmental).
Show a trend over time	Line Chart [41] [40]	Plotting the annual number of publications on PFAS remediation technologies.
Display the relationship between two variables	Scatter Plot [42]	Correlating chemical potency with molecular weight across a class of compounds.
Show the frequency distribution of a dataset	Histogram [43]	Visualizing the distribution of reported half-lives of a chemical in the environment.
Present precise numerical values for detailed analysis	Table [40] [42]	Listing all study parameters, results, and quality appraisal scores for expert review.

The Scientist's Toolkit: Research Reagent Solutions

This section details essential tools and materials for constructing and maintaining a web-based evidence inventory, framed as a "Research Reagent" list.

Table 3: Essential Research Reagents for Building Evidence Inventories

Tool/Reagent	Function & Explanation
Estuary Flow	A real-time data integration platform used to extract and stream data from source databases and APIs directly into the evidence inventory, ensuring data is current [35].
Browse AI	An AI-powered tool that automates web scraping from scientific databases and journals that lack a direct API, capturing data points like publication titles and abstracts [36].
Octoparse	A web scraping tool useful for extracting data from complex websites, including those with dynamic content powered by JavaScript. It can handle large-scale data extraction tasks [35] [38].
Structured Query Language (SQL) Database	The foundational technology for storing and querying the structured evidence data. It enables fast, complex queries from the web interface (e.g., "show all inhalation toxicity studies for Chemical X") [35].
JavaScript Visualization Library (e.g., D3.js)	A programming library used to create interactive charts and graphs (e.g., interactive histograms, scatter plots) within the web browser, allowing users to visually explore the evidence base [41] [40].
Trifluoperazine dimaleate	Trifluoperazine dimaleate, CAS:605-75-4, MF:C29H32F3N3O8S, MW:639.6 g/mol
DL-Tboa	DL-Tboa, CAS:208706-75-6, MF:C11H13NO5, MW:239.22 g/mol

The creation of interactive, web-based evidence inventories through systematic data extraction and structuring transforms static literature collections into dynamic resources for scientific assessment. By implementing the protocols for automated extraction, hybrid data capture, and thoughtful visualization outlined in this document, researchers can build powerful tools that illuminate evidence patterns and gaps. When deployed within the context of systematic evidence mapping for chemicals, these inventories significantly enhance the efficiency, transparency, and utility of the assessment process, ultimately supporting more informed public health and environmental decisions.

Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals valued for their resistance to heat, oils, stains, grease, and water. This same stability makes them persistent in the environment and the human body, earning them the nickname "forever chemicals" [44]. The U.S. Environmental Protection Agency (EPA) employs a multi-faceted, science-based framework to assess the risks of PFAS, which includes several key tools and data streams rather than a single dashboard. This application note details the quantitative data sources, experimental protocols, and computational tools that form this comprehensive assessment framework, providing researchers with practical methodologies for PFAS investigation within the context of systematic evidence mapping for chemical assessment.

The EPA's assessment relies on quantifiable data from multiple regulatory and monitoring programs. The key data sources provide structured information on occurrence, waste management, and environmental releases.

Table 1: Key Quantitative Data Streams for PFAS Assessment

Data Source	Reported Parameters	Recent Findings (2023-2025)	Regulatory Context
Toxic Release Inventory (TRI) [45]	- 189 PFAS tracked (2023)- Release quantities by medium (air, water, land)- Waste management methods (treatment, energy recovery)- Sector-specific data (e.g., chemical manufacturing, hazardous waste)	- 740,000 lb increase in PFAS waste managed (2020-2023)- Hazardous waste management sector reported 82% of all 2023 releases- 95% of on-site land disposal goes to RCRA Subtitle C landfills	Reporting threshold: 100 lbs; PFAS are designated "chemicals of special concern" (effective 2024)
PFAS Exposure Risk Dashboard [44]	- Contaminated locations (>2,991 as of 6/2025)- Public water system testing results- Population served by affected systems- Annual intake from food/water (ng)	- 1,272 of 2,593 tested systems in MI/NY/PA detected PFAS- Typical annual intake: Food (3,440 ng) vs. Water (3,088 ng)- PA shows highest combined intake; MI the lowest	Complements federal data with state-level (MI, NY, PA) testing and intake estimates
Drinking Water Standards [46] [47]	- Maximum Contaminant Levels (MCLs) for 6 PFAS- Monitoring and public notification requirements	- First nationwide PFAS drinking water standards established April 2024- Systems must comply by April 2029	EPA's rule is being challenged in court; some states (CA, NY, MI) have stricter standards

Systematic Evidence Mapping and Data Synthesis Protocols

Systematic Evidence Maps (SEMs) are critical tools for organizing the extensive and growing body of PFAS research. They provide an interactive inventory of scientific studies, helping to identify evidence gaps and inform risk assessments [21].

Protocol for Literature Identification and Screening

Objective: To systematically identify, screen, and categorize scientific literature on PFAS for evidence mapping.

Workflow:

• Search Strategy Development: Define research questions and formulate search strings using key terms (e.g., "PFAS," "perfluoroalkyl," specific compounds like PFOA/PFOS) combined with exposure, health effect, and environmental fate terms. Utilize multiple databases (PubMed, Web of Science, Scopus).
• Literature Retrieval: Execute searches and merge results using reference management software. Remove duplicate entries.
• Title/Abstract Screening: Two independent reviewers screen titles and abstracts against pre-defined inclusion/exclusion criteria (e.g., original data, relevant PFAS, human/ecological endpoints).
• Full-Text Review: Retrieve and review full-text articles for studies passing the initial screen. Document reasons for exclusion.
• Data Extraction: Code included studies for key elements: PFAS studied, population/exposure system, study design, endpoints measured, and results.
• Evidence Mapping: Synthesize extracted data into interactive visualizations showing evidence distribution and gaps.

Analytical Methodologies for Environmental Matrices

Objective: To provide standardized methods for detecting and quantifying PFAS in diverse environmental samples.

EPA Method 1633 [48]:

• Application: Measures 40 specific PFAS compounds in wastewater, surface water, groundwater, soil, biosolids, sediment, and landfill leachate.
• Workflow:
  • Sample Collection: Use polyethylene or polypropylene containers. Preserve samples with ammonium acetate and refrigerate.
  • Extraction: Solid samples undergo ion exchange solid-phase extraction (SPE). Liquid samples are directly processed via SPE.
  • Analysis: Analyze extracts using liquid chromatography with tandem mass spectrometry (LC-MS/MS).
  • Quality Control: Include laboratory blanks, matrix spikes, and duplicate samples to ensure accuracy and precision.

EPA Method 1621 [48]:

• Application: A screening method that measures the aggregate concentration of organofluorines in wastewater as Adsorbable Organic Fluorine (AOF).
• Workflow:
  • Adsorption: Pass the water sample through activated carbon to adsorb organic compounds.
  • Combustion: Combust the activated carbon in a stream of oxygen.
  • Measurement: Quantify the released hydrogen fluoride using ion chromatography or a fluoride-ion selective electrode.

Visualization of Assessment Workflows

The following diagrams illustrate the logical relationships and workflows central to the EPA's PFAS assessment strategy and the systematic evidence mapping process.

PFAS Regulatory Risk Assessment Workflow

Systematic Evidence Mapping Process

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for PFAS Research and Analysis

Research Reagent / Material	Function / Application	Example Use in Protocol
LC-MS/MS Grade Solvents (e.g., Methanol, Acetonitrile)	High-purity solvents for sample extraction, mobile phase preparation, and instrument calibration to minimize background interference.	Used in sample preparation and as the mobile phase in EPA Method 1633 for precise chromatographic separation and detection.
Solid-Phase Extraction (SPE) Cartridges (e.g., WAX, C18)	Extraction and clean-up of PFAS from complex environmental matrices (water, soil, biosolids) to concentrate analytes and remove interfering substances.	Critical component in EPA Method 1633 for isolating target PFAS compounds from samples prior to instrumental analysis.
Isotopically Labeled PFAS Internal Standards (e.g., ¹³C-PFOA, ¹³C-PFOS)	Internal standards added to samples to correct for analyte loss during sample preparation and matrix effects during MS analysis, ensuring quantitative accuracy.	Spiked into all samples, blanks, and standards in EPA Method 1633 to serve as a quality control and quantification reference.
Certified Reference Standards (Mixtures of target PFAS)	Calibration standards used to generate instrument calibration curves, ensuring accurate identification and quantification of PFAS.	Used to calibrate the LC-MS/MS system in both Methods 1633 and 1621, providing the basis for all concentration measurements.
Activated Carbon	Adsorption medium for trapping organic fluorine compounds from water samples in aggregate parameter methods.	The key adsorbent material in EPA Method 1621 for measuring Adsorbable Organic Fluorine (AOF).
Semapimod	Semapimod, CAS:352513-83-8, MF:C34H52N18O2, MW:744.9 g/mol	Chemical Reagent
Magnesium lithospermate B	Magnesium lithospermate B, MF:C36H28MgO16, MW:740.9 g/mol	Chemical Reagent

Systematic Evidence Maps (SEMs) are valuable tools that systematically capture and screen scientific literature, providing an interactive inventory of relevant research [21]. Within the context of chemical assessment, the integration of New Approach Methodologies (NAMs) is essential for modernizing risk assessment practices. NAMs are defined as emerging technologies, methodologies, approaches, or combinations thereof, with the potential to improve risk assessment, fill critical information gaps, and reduce reliance on animal studies [49]. This document outlines detailed application notes and protocols for incorporating mechanistic data and toxicokinetics into SEMs, providing a structured framework for researchers and risk assessors.

Compendium of New Approach Methodologies (NAMs)

The following table summarizes the key categories of NAMs, their descriptions, and primary applications in chemical risk assessment, integrating both in vitro and in silico approaches [49].

Table 1: Categories of New Approach Methodologies (NAMs) for Chemical Risk Assessment

NAM Category	Description	Example Techniques	Primary Applications in Risk Assessment
High-Throughput In Vitro Assays	Automated screening of chemicals for bioactivity across numerous cellular pathways.	ToxCast assay pipeline; high-content screening.	Hazard identification; prioritization of chemicals for further testing; hypothesis generation [50] [49].
Advanced In Vitro Models	More physiologically relevant cell cultures that better mimic human tissues.	3D cell cultures; organoids; spheroids; microphysiological systems (MPS) or "organs-on-chips" [49].	Improved hazard characterization; toxicodynamic evaluation; investigation of cell- and tissue-specific effects.
OMICS Technologies	Analysis of global molecular profiles within a biological system.	Transcriptomics; proteomics; metabolomics.	Uncovering mechanisms of toxicity; identifying biomarkers of effect and exposure; developing Adverse Outcome Pathways (AOPs) [49].
Computational & In Silico Models	Computer-based methods to predict chemical properties, toxicity, and fate in the body.	QSAR; read-across; molecular docking; PBPK models; systems biology models [49].	Filling data gaps for untested chemicals; category formation; risk translation across species and exposure scenarios.
Integrated Approaches for Testing and Assessment (IATA)	Structured frameworks that combine multiple sources of evidence to conclude on chemical toxicity.	Weight-of-evidence approaches integrating in vitro, in silico, and existing in vivo data [49].	Regulatory decision-making for hazard identification and characterization; supporting grouping and read-across.

Experimental Protocols for a Tiered NGRA Framework

The following protocol details a tiered Next-Generation Risk Assessment (NGRA) framework, using pyrethroid insecticides as a case study, for integrating bioactivity and toxicokinetic data into a structured assessment [50].

Protocol 1: Tiered NGRA for Cumulative Risk Assessment

Objective: To systematically evaluate the combined risk of chemical exposures using a tiered approach that integrates toxicokinetics (TK) with toxicodynamics (TD) data from NAMs.

Workflow Overview:

Materials and Reagents:

• Chemical Library: Compounds of interest (e.g., bifenthrin, cyfluthrin, cypermethrin, deltamethrin, lambda-cyhalothrin, permethrin).
• Bioactivity Data Source: ToxCast database (accessed via the CompTox Chemicals Dashboard) [50].
• TK Modeling Software: Open-source tools (e.g., httk R package) or commercial software (e.g., GastroPlus) [50] [51].
• Exposure Data: Human biomonitoring data or exposure estimations from dietary models (e.g., EFSA's PRIMo) [50].

Procedure:

• Tier 1: Bioactivity Data Gathering and Hypothesis Generation.
  • Step 1.1: Gather in vitro bioactivity data from the ToxCast database for the target chemicals.
  • Step 1.2: Calculate average AC50 (concentration at 50% activity) values. Categorize data by gene targets (e.g., androgen receptor, cytochrome P450) and tissue systems (e.g., liver, brain) to establish bioactivity indicators.
  • Step 1.3: Use these indicators to form initial hypotheses regarding potential hazards and modes of action.

• Tier 2: Exploring Combined Risk Assessment.

  • Step 2.1: Calculate relative potencies for each pyrethroid normalized to the most potent chemical in each category using the formula: Relative Potency = (Most Potent AC50) / (Chemical-specific AC50).
  • Step 2.2: Compare relative potency patterns from ToxCast with those derived from traditional points of departure (e.g., NOAELs from regulatory assessments). Visually analyze patterns using radial charts.
  • Step 2.3: Accept or reject the hypothesis of a common mode of action based on the coherence (or lack thereof) between in vitro bioactivity and in vivo toxicity data [50].

• Tier 3: TK Modeling and Margin of Exposure (MoE) Analysis.

  • Step 3.1: Use TK modeling (e.g., with httk) to estimate internal human concentrations (Cmax) for each pyrethroid based on realistic exposure scenarios (e.g., dietary exposure estimations).
  • Step 3.2: Calculate bioactivity-based MoEs using the formula: MoE = In vitro POD (e.g., AC50) / Estimated Plasma Cmax.
  • Step 3.3: Identify critical tissue-specific pathways driving the potential risk based on the internal dose estimates [50].

• Tier 4: Refining the Bioactivity Assessment.

  • Step 4.1: Refine the bioactivity indicators by comparing in vitro effect concentrations with estimated interstitial concentrations from in vivo studies using TK models.
  • Step 4.2: Evaluate the uncertainty in intracellular concentration estimations. A sensitivity analysis can be performed to determine the impact of using nominal versus free concentrations in the assay media on the final risk classification [51].

• Tier 5: Integrated Risk Characterization.

  • Step 5.1: Synthesize data from all tiers. Determine if the MoE for dietary exposure is sufficient (e.g., within standard safety factors).
  • Step 5.2: Account for aggregate exposure from all relevant sources (e.g., biocides, pharmaceuticals) and identify any remaining data gaps for final decision-making [50].

The Scientist's Toolkit: Essential Research Reagents and Solutions

The following table catalogs key reagents, tools, and databases critical for implementing NAMs in chemical assessment research [50] [49] [51].

Table 2: Key Research Reagent Solutions for NAM-Based Chemical Assessment

Tool/Reagent	Type	Function and Application
ToxCast Database	Database	Provides a large repository of high-throughput screening data on chemical bioactivity across a wide range of cellular pathways, used for hazard identification and potency estimation [50] [49].
httk R Package	Software Tool	An open-source, high-throughput toxicokinetics package used to predict chemical absorption, distribution, metabolism, and excretion (ADME) in humans, facilitating the translation of external dose to internal concentration [51].
Physiologically Based Kinetic (PBK) Models	Computational Model	Mechanistic models that simulate the fate of chemicals in the body over time. Used for in vitro to in vivo extrapolation (IVIVE), exposure reconstruction, and accounting for population variability [49].
Adverse Outcome Pathway (AOP) Framework	Knowledge Framework	Organizes mechanistic knowledge into a sequence of events from a molecular initiating event to an adverse outcome at the organism level. Serves as a central framework for designing and interpreting NAM data [49].
OECD QSAR Toolbox	Software Tool	A software application that supports the use of (Q)SAR models and read-across for filling data gaps by predicting properties and toxicity based on chemical structure [49].
Bioactivity-Exposure Ratio (BER)	Risk Metric	A key metric in NGRA, calculated as the ratio between a bioactivity point of departure (e.g., from an in vitro assay) and an estimated human internal exposure. A BER > 1 typically indicates a low potential for risk [51].
Reumycin	Reumycin|Antitumor Antibiotic|Research Compound	Reumycin is an antitumor antibiotic for research. Shown to affect blood coagulation and platelet counts in studies. For Research Use Only. Not for human use.
Cefuzonam	Cefuzonam, CAS:82219-78-1, MF:C16H15N7O5S4, MW:513.6 g/mol	Chemical Reagent

Workflow for Systematic Evidence Mapping with NAM Integration

The process of building a Systematic Evidence Map is enhanced by the targeted inclusion of NAM data. The following diagram outlines this integrated workflow.

Overcoming Implementation Challenges and Optimizing SEM Workflows

In chemical assessment and drug development, critical data on catalytic materials, reaction mechanisms, and synthesis parameters are often buried within vast, fragmented literature comprising hundreds of thousands of publications, patents, and proprietary reports [52]. This data heterogeneity poses a significant challenge to innovation, as no individual researcher can comprehensively review all relevant information. Knowledge graph (KG) technology addresses this fundamental problem by providing a unified framework for representing conceptual knowledge, transforming disconnected data into a structured, machine-readable, and human-interpretable semantic network [52].

Knowledge graphs organize information as interconnected entities (nodes) and relationships (edges), creating a comprehensive web of knowledge that links chemical compositions, synthesis parameters, characterization data, and catalytic performance into a single, semantically consistent network [52]. This structured approach enables researchers to navigate complex relationship webs in heterogeneous catalysis and other chemical domains, connecting catalysts to reactions, conditions, mechanisms, and outcomes in a queryable format. By adopting FAIR principles (Findable, Accessible, Interoperable, Reusable), knowledge graphs ensure that both experimental and computational data can be readily employed across research initiatives [52], making them particularly valuable for creating systematic evidence maps in chemical assessment research.

Quantitative Evidence of Knowledge Graph Applications

Recent implementations demonstrate the substantial scale and impact of knowledge graphs across chemical and materials science domains. The table below summarizes key metrics from prominent knowledge graph deployments:

Table 1: Scale and Applications of Knowledge Graphs in Scientific Research

Domain/System	Graph Scale	Data Sources	Primary Application Areas
Framework Materials (KG-FM) [53]	2.53 million nodes, 4.01 million relationships	>100,000 articles on MOFs, COFs, HOFs	Material property retrieval, question-answering systems, trend analysis
Heterogeneous Catalysis KGs [52]	Not specified (large-scale)	Journal articles, patents, lab reports, databases	Catalyst discovery, reaction optimization, hypothesis generation
OntoRXN [54]	Not specified (domain-specific)	ioChem-BD computational chemistry calculations	Reaction network analysis, mechanistic studies
Drug Discovery KGs [55]	Various sizes	Biomedical literature, chemical databases, genomic data	Target identification, drug repurposing, side-effect prediction

The implementation of KG-FM for framework materials illustrates how knowledge graphs effectively address data heterogeneity. By analyzing over 100,000 articles, this comprehensive knowledge graph covers synthesis, properties, and applications of metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs) [53]. When integrated with large language models (LLMs), this knowledge graph achieved a remarkable 91.67% accuracy rate in question-answering tasks, significantly outperforming standalone models like GPT-4 (33.33% accuracy) [53]. This performance differential highlights the value of knowledge graphs in providing precise, verifiable information with traceable sources, a critical requirement for chemical assessment research and evidence mapping.

Protocols for Knowledge Graph Implementation

Protocol: Constructing a Domain-Specific Knowledge Graph for Chemical Assessment

Purpose: To establish a structured methodology for building a knowledge graph that integrates heterogeneous chemical data from multiple sources for systematic evidence mapping.

Materials and Reagents:

• Computational resources (server with minimum 16GB RAM, 500GB storage)
• Neo4j graph database platform (version 5.12.0 or higher) [53]
• Python programming environment (3.8+ with required libraries)
• Large Language Model access (e.g., Qwen2-72B) [53]
• Data sources (Web of Science API, internal databases, published literature)

Procedure:

• Data Collection and Preprocessing

  • Retrieve relevant journal articles from scientific databases (e.g., Web of Science) using domain-specific search queries [53].
  • Export abstracts and publication details (DOI, authors, publication date, journal information) in structured text format (TXT files) for processing [53].
  • Implement data cleaning scripts to normalize terminology and resolve entity inconsistencies across sources.

• Ontology Design and Schema Definition

  • Define core classes specific to chemical assessment (e.g., ChemicalSpecies, ReactionStep, ExperimentalCondition, ToxicityEndpoint) based on existing ontologies like OntoRXN [54] and OntoCompChem [54].
  • Establish relationship types between classes (e.g., HAS_PROPERTY, UNDERGOES_REACTION, HAS_TOXICITY_PROFILE).
  • Formalize the ontology using Web Ontology Language (OWL) to enable semantic reasoning and interoperability [54].

• Information Extraction with LLMs

  • Implement prompt engineering to extract structured information from unstructured text using LLMs like Qwen2-72B [53].
  • Convert extracted information into JSON format with logical relations between entities [53].
  • Manually validate extraction accuracy by reviewing 100+ results against source material, calculating True Positive rates to ensure quality (target: >98% TP rate) [53].

• Graph Population and Database Creation

  • Use Cypher query language to import processed node and relationship data into Neo4j graph database [53].
  • Establish connections between structured metadata (titles, DOI numbers) and nodes parsed by LLM through defined relationships like "Derived from" and "Published in" [53].
  • Implement indexing strategies for optimal query performance on large-scale graphs.

• Validation and Quality Assurance

  • Execute SPARQL queries to verify graph structure and relationship integrity [54].
  • Cross-validate extracted relationships against trusted domain sources.
  • Implement version control mechanisms to track graph evolution and updates.

Protocol: Querying and Utilizing Knowledge Graphs for Evidence Synthesis

Purpose: To enable researchers to extract meaningful insights and identify evidence patterns from constructed knowledge graphs.

Materials:

• Access to populated knowledge graph instance
• SPARQL or Cypher query interface
• Data visualization tools (Neo4j Browser, custom dashboards)

Procedure:

• Query Formulation

  • Identify research questions relevant to chemical assessment goals.
  • Formulate graph queries using Cypher or SPARQL to traverse relationships and retrieve connected entities.
  • Implement parameterized queries for reusable evidence retrieval patterns.

• Retrieval-Augmented Generation (RAG) Integration

  • Generate Cypher queries automatically based on natural language questions using LLM integration [53].
  • Execute queries against the knowledge graph to retrieve relevant structured data [53].
  • Formulate precise, evidence-based answers using the retrieved data combined with LLM capabilities [53].

• Evidence Mapping and Visualization

  • Extract subgraphs related to specific chemical assessments or mechanistic pathways.
  • Apply graph algorithms to identify central nodes, clusters, and connectivity patterns.
  • Generate visual representations of evidence networks for interpretation and reporting.

Workflow Visualization

Figure 1: Knowledge Graph Construction and Application Workflow

Figure 2: Knowledge Graph Structure for Catalytic Reaction Data

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 2: Key Research Reagent Solutions for Knowledge Graph Implementation

Tool/Resource	Type/Function	Application in KG Development
Neo4j [53]	Graph Database Platform	Primary storage and querying of knowledge graph entities and relationships
SPARQL [54]	Query Language	Querying RDF-based knowledge graphs and retrieving interconnected data
Cypher [53]	Query Language	Native query language for Neo4j graph databases
OWL (Web Ontology Language) [54]	Ontology Language	Formal representation of domain knowledge with rich semantics
Large Language Models (e.g., Qwen2-72B) [53]	Natural Language Processing	Automated extraction of structured information from unstructured text
ioChem-BD [54]	Computational Chemistry Database	Source of preprocessed computational data in CML format
Chemical Markup Language (CML) [54]	Data Format	Standardized representation of chemical information for interoperability
OntoRXN [54]	Domain Ontology	Specialized ontology for representing reaction networks
Python [53]	Programming Language	Scripting and automation of data processing and graph operations
Chaetoglobosin C	Chaetoglobosin C, CAS:50645-76-6, MF:C32H36N2O5, MW:528.6 g/mol	Chemical Reagent
Roselipin 2B	Roselipin 2B, MF:C42H74O15, MW:819.0 g/mol	Chemical Reagent

In the context of systematic evidence maps for chemical assessment research, the choice of data management architecture is pivotal. The "schema-first" and "schemaless" approaches represent two fundamentally different philosophies for organizing scientific data. A schema-first approach requires a predefined, rigid structure for data before any information can be stored, enforcing consistency and validity at the point of entry. In contrast, a schemaless approach (more accurately described as "schema-on-read") allows data to be stored without a predefined structure, offering flexibility to accommodate heterogeneous or evolving data types commonly encountered in research environments [56] [57]. This document outlines detailed application notes and experimental protocols for implementing these approaches within chemical assessment and drug development research.

Comparative Analysis: Schemaless vs. Schema-First

The decision between schemaless and schema-first architectures involves significant trade-offs. The following table summarizes the core characteristics of each approach:

Table 1: Fundamental Characteristics of Data Management Approaches

Feature	Schema-First Approach	Schemaless Approach
Core Principle	Structure is explicitly defined and enforced before data entry [56] [58]	Structure is interpreted at the time of data reading or application use [57]
Data Integrity	High; enforced by database constraints (e.g., referential integrity) [58]	Application-dependent; pushed from the database to the application layer [56]
Development Speed (Initial)	Slower due to upfront design effort	Faster; allows for rapid prototyping without schema definition [57]
Flexibility & Evolution	Requires formal migration procedures to alter structure [58]	High; easily accommodates new data types and evolving requirements [57]
Best-Suited Data Types	Structured, uniform, and consistent data [56]	Non-uniform, heterogeneous, or complex hierarchical data [57]

The practical implications of these characteristics for research are further detailed below:

Table 2: Research Application and Implications

Aspect	Schema-First Approach	Schemaless Approach
Ideal Research Use Cases	Well-defined experimental data, validated assay results, chemical registry systems	Exploratory research, heterogeneous data integration, evolving evidence maps
Data Modeling Process	Top-down; domain is modeled into a fixed relational schema [58]	Bottom-up; domain is modeled using application code or flexible constructs [56]
Interoperability & Collaboration	Standardized interface simplifies collaboration across teams	Flexibility can lead to multiple implicit schemas, complicating integration [57]
Long-Term Maintenance	Clear contract simplifies understanding for new maintainers [57]	Hidden implicit schema can slow down further development and analysis [57]

Application Notes for Chemical Assessment Research

Implementing a Schema-First Protocol for Systematic Evidence Mapping

The schema-first approach provides a robust foundation for managing structured evidence data.

Experimental Protocol 1: Schema-First Evidence Cataloging

Objective: To create a definitive, queryable database of scientific studies for chemical risk assessment using a predefined schema.

Materials and Reagents:

• Relational Database Management System (RDBMS): (e.g., PostgreSQL, MySQL) to host the structured schema and enforce data integrity.
• Schema Design Tool: Software for visually creating and modeling Entity-Relationship Diagrams (ERDs).

Methodology:

• Domain Analysis: Collaborate with domain experts to identify core entities (e.g., Chemical, Study, Assay, Endpoint, Author) and their attributes.
• Schema Design (DDL): Define the database schema using SQL Data Definition Language (DDL). This includes tables, columns, data types, primary keys, and foreign key constraints to enforce relationships.

• Data Ingestion and Validation: Develop scripts to ingest and validate data from source materials (e.g., journal articles, lab notebooks) against the predefined schema. Data failing constraint checks is rejected.
• Query and Analysis: Execute structured queries (SQL) to generate evidence maps, for example, to count all studies for a specific chemical or aggregate results by toxicity endpoint.

Implementing a Schemaless Protocol for Integrative Knowledge Graphs

The schemaless paradigm is exceptionally suited for building comprehensive knowledge graphs that integrate disparate data sources, a common challenge in chemical assessment.

Experimental Protocol 2: Schemaless Knowledge Graph Construction

Objective: To integrate heterogeneous data sources—including structured assay results, unstructured text from literature, and public chemical databases—into a unified knowledge graph for holistic analysis.

Materials and Reagents:

• Graph Database or Document Store: A database such as Neo4j (property graph) or MongoDB (document model) that supports flexible, nested data structures [56] [59].
• ETL (Extract, Transform, Load) Pipeline Tools: Frameworks for extracting data from multiple sources and loading it into the target datastore.

Methodology:

• Data Acquisition: Extract data from diverse sources (e.g., PubChem, TOXNET, internal PDF reports) without the need to conform to a single, unified structure.
• Data Normalization (Minimal): Perform essential cleaning and standardize identifiers (e.g., convert all chemical names to InChIKeys) to enable entity linking, while preserving the original structure of the data.
• Graph Ingestion: Ingest the normalized data into the graph database. Each entity becomes a node, and relationships become edges. New node and edge types can be added without restructuring the entire database. Example: A document stored in a schemaless database for a study might look like:

• Query and Traversal: Use graph query languages (e.g., Cypher for Neo4j) to perform complex, multi-hop traversals across the integrated data, such as finding all pathways through which a chemical and its metabolites are predicted to interact.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools and Technologies for Data Management in Research

Tool/Reagent	Function	Typical Use Case
PostgreSQL	Open-source, object-relational database system	Implementing a robust, schema-first database for structured research data [60]
MySQL	Popular open-source RDBMS, used as storage backend	Powering scalable datastores, often as part of a larger architecture [60]
MongoDB	Document-oriented NoSQL database program	Structuring chemical and assay data in flexible JSON-like documents [59]
Neo4j	Native graph database platform	Building knowledge graphs to map complex chemical-biological interactions [56]
TigerGraph	Scalable graph database for enterprise	Handling large-scale, highly interconnected data for advanced analytics [56]
GraphQL	Query language and runtime for APIs	Providing a flexible API layer for front-end clients to query evidence maps, regardless of the backend data store [61] [62] [63]
Quizalofop-P	Quizalofop-P Herbicide	Quizalofop-P is a selective herbicide targeting ACCase, used in agricultural research to control grass weeds. For Research Use Only. Not for personal use.

Visualizing Workflows and Logical Relationships

Diagram 1: High-level workflow for selecting between schemaless and schema-first approaches in research data management.

Diagram 2: Detailed protocol for constructing a schemaless knowledge graph for chemical assessment.

Ensuring Scalability and Interoperability for Complex EH Data

Systematic Evidence Maps (SEMs) represent a transformative methodology for addressing the complex challenges of environmental health (EH) data, particularly in chemical assessment research. Unlike systematic reviews with their narrowly focused questions, SEMs provide queryable databases of systematically gathered evidence that characterize broad features of the evidence base, enabling researchers to identify trends, knowledge gaps, and critical data clusters for further analysis [64]. The successful implementation of SEMs for complex EH data necessitates robust frameworks addressing scalability through knowledge graph technologies and interoperability through standardized semantic frameworks. This application note provides detailed protocols and methodologies for constructing SEMs that can handle the heterogeneous, interconnected nature of modern EH data while ensuring compatibility with evolving regulatory requirements including the European Health Data Space (EHDS) and AI Act provisions [65] [66].

Environmental health research generates complex, heterogeneous data from diverse sources including mammalian animal bioassays, epidemiological studies, in vitro model systems, and New Approach Methodologies (NAMs) [20]. Systematic Evidence Mapping has emerged as a critical tool for contextualizing this evidence within chemical assessment workflows. SEMs function as comprehensive, queryable summaries of large bodies of policy-relevant research, supporting trend identification and forward-looking predictions in chemical risk sciences [64].

The fundamental value proposition of SEMs lies in their ability to organize and characterize an evidence base for exploration by diverse end-users with varied research interests [66]. For chemical assessment and drug development professionals, this facilitates resource-efficient priority setting by identifying evidence clusters suitable for systematic review while highlighting critical knowledge gaps requiring additional primary research [64]. As regulatory agencies including the U.S. EPA Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) programs increasingly adopt systematic evidence mapping, standardized approaches to ensuring scalability and interoperability become essential components of regulatory-grade research infrastructure [20].

Foundational Concepts and Definitions

Systematic Evidence Maps

Systematic Evidence Maps are defined as queryable databases of systematically gathered research that extract and structure data and/or metadata for exploration following a rigorous methodology aimed at minimizing bias and maximizing transparency [66]. As illustrated in Table 1, SEMs perform distinct but complementary functions to systematic reviews in evidence-based decision-making.

Table 1: Comparative Functions of Evidence Synthesis Methodologies

Methodology	Primary Function	Scope	Resource Requirements	Output
Systematic Evidence Map	Evidence characterization and organization	Broad evidence base scoping	Moderate to High	Queryable database, evidence catalogs, gap analysis
Systematic Review	Evidence synthesis and meta-analysis	Narrowly focused question	High	Quantitative synthesis, strength of evidence assessment
Targeted Literature Review	Rapid evidence assessment	Focused on immediate needs	Low to Moderate	Narrative summary, limited critical appraisal

Interoperability Framework Components

Interoperability in EH data systems operates across multiple dimensions. The European Interoperability Framework (EIF) conceptual model comprises four levels: legal interoperability (aligning legislation and policies), organizational interoperability (coordinating processes and responsibilities), semantic interoperability (ensuring precise meaning of exchanged information), and technical interoperability (linking systems and services) [65]. The Refinement of the eHealth European Interoperability Framework (ReEIF) expands this to six layers specifically tailored for healthcare and EH contexts, adding conceptual and process layers to the foundational framework [65].

Scalability Solutions for Complex EH Data

Knowledge Graphs as Scalable Data Structures

Traditional systematic mapping methods relying on rigid, flat data tables and schema-first approaches are ill-suited to the highly connected, heterogeneous nature of EH data [66]. Knowledge graphs offer a flexible, schemaless, and scalable model for systematically mapping EH literature by representing data as networks of nodes (entities) and edges (relationships). This graph-based structure provides significant advantages for SEM implementation:

• Flexibility: Accommodate evolving data models without restructuring entire databases
• Relationship-centric modeling: Explicitly capture complex interactions between chemical exposures, health outcomes, and study parameters
• Semantic richness: Integrate with domain ontologies for enhanced query capabilities
• Scalability: Efficiently handle large-scale, interconnected datasets common in EH research

Table 2: Scalability Assessment of Data Storage Technologies for SEMs

Storage Technology	Data Model Flexibility	Relationship Handling	Query Performance	Integration with Ontologies
Relational Databases	Low (fixed schema)	Limited (requires joins)	Moderate for complex queries	Limited
Flat File Structures	Moderate (schema-on-read)	Poor	Low for complex relationships	Poor
Knowledge Graphs	High (schemaless)	Excellent (native relationships)	High for connected data	Excellent

Implementation Protocol: Knowledge Graph Construction for EH SEMs

Objective: Construct a scalable knowledge graph for environmental health Systematic Evidence Mapping.

Materials and Software Requirements:

• Graph database platform (Neo4j, Amazon Neptune, or Azure Cosmos DB)
• Data extraction and annotation tools (SQL/NoSQL databases, XML/JSON parsers)
• Ontology management system (Protégé or WebProtégé)
• Programming environment (Python/R with graph analysis libraries)

Procedure:

• Domain Analysis and Ontology Selection

  • Identify core entity types relevant to the EH domain (chemicals, health outcomes, study designs, experimental models)
  • Select established ontologies (CHEBI for chemicals, DOID for diseases, OBI for study designs)
  • Define custom ontology extensions where domain coverage is limited

• Data Extraction and Entity Recognition

  • Implement automated extraction of structured data from study reports
  • Apply natural language processing for entity recognition from unstructured text
  • Resolve entity disambiguation using authoritative vocabularies

• Graph Schema Design

  • Define node labels and properties for each entity type
  • Establish relationship types between entities (e.g., ADMINISTEREDTO, MEASUREDOUTCOME)
  • Implement property graph model with appropriate indexing strategies

• Data Loading and Quality Assurance

  • Batch import extracted data using graph database loaders
  • Implement consistency checks for node and relationship properties
  • Validate semantic consistency across ontology mappings

• Query Interface Development

  • Implement Cypher or Gremlin query templates for common evidence mapping questions
  • Develop RESTful APIs for programmatic access to the knowledge graph
  • Create visualization dashboards for graph exploration and analysis

Diagram 1: Knowledge graph implementation workflow for scalable EH data management (Max Width: 760px)

Interoperability Framework Implementation

Standards-Based Semantic Interoperability

Achieving semantic interoperability in EH SEMs requires implementation of standardized terminologies and exchange protocols. The European Health Data Space (EHDS) for secondary use of data (EHDS2) establishes a regulatory-driven framework for cross-border health data exchange that increasingly impacts EH research [65]. Core standards for interoperability include:

• FHIR (Fast Healthcare Interoperability Resources): RESTful APIs with JSON/XML formatting for real-time data sharing, now supported by over 90% of EHR vendors as their interoperability baseline [67] [68]
• HL7 v2 and CDA: Legacy standards for clinical document architecture and messaging, particularly in laboratory data exchange
• SNOMED CT: Comprehensive clinical terminology system for standardizing health terminology
• OMOP Common Data Model: Standardized vocabulary and schema for observational health data

Protocol: Implementing the EHDS2 Interoperability Framework

Objective: Implement an EHDS2-aligned interoperability framework for cross-border EH data exchange in SEMs.

Materials:

• FHIR server implementation (HAPI FHIR, Microsoft FHIR Server)
• Terminology service (Ontoserver, Snow Owl)
• Compliance checking tools for GDPR, AI Act, and EHDS regulations
• Data use and access management system

Procedure:

• Regulatory Compliance Assessment

  • Map data types and uses against GDPR provisions for scientific research
  • Assess AI system compliance with AI Act risk categorization requirements
  • Document legal bases for secondary data use under EHDS2 framework

• Semantic Harmonization

  • Map local terminologies to standard ontologies (SNOMED CT, LOINC, CHEBI)
  • Implement FHIR profiles constraining resources for EH-specific data elements
  • Establish terminology service for continuous vocabulary maintenance

• Technical Infrastructure Deployment

  • Deploy FHIR RESTful APIs for data access and exchange
  • Implement OAuth2-based authentication and authorization
  • Configure audit logging for data access compliance monitoring

• Data Quality Framework Implementation

  • Define data quality metrics aligned with EHDS2 requirements
  • Implement automated quality validation pipelines
  • Establish data provenance tracking mechanisms

• Cross-Border Testing and Validation

  • Execute test transactions with EHDS2-connected nodes
  • Validate semantic interoperability through clinical concept representation
  • Perform security penetration testing and vulnerability assessment

Table 3: Interoperability Standards Implementation Matrix

Standard	Maturity in EH	Primary Use Case	Implementation Priority
FHIR R4	High	Real-time data exchange, API-based integration	Critical
HL7 v2	Medium	Legacy system integration, lab data messaging	High (for existing systems)
OMOP CDM	High	Observational research data standardization	High for regulatory submissions
SNOMED CT	Medium-High	Semantic interoperability, terminology services	Critical for EHDS2 compliance
OpenEHR	Medium	Clinical data modeling, decision support	Medium (emerging importance)

Diagram 2: Layered interoperability framework for EH data exchange (Max Width: 760px)

Integrated SEM Implementation Protocol

End-to-End SEM Development Workflow

Objective: Construct a scalable, interoperable Systematic Evidence Map for chemical assessment.

Materials:

• Systematic review software (DistillerSR, Rayyan, CADIMA)
• Machine learning tools for study prioritization (SWIFT-Review, ASReview)
• Graph database platform
• FHIR-compliant data exchange infrastructure
• Visualization tools (Tableau, R Shiny, Python Dash)

Procedure:

• Problem Formulation and PECO Development

  • Define broad Populations, Exposures, Comparators, and Outcomes criteria
  • Identify supplemental content categories (NAMs, genotoxicity, pharmacokinetics)
  • Establish evidence stream classification framework

• Search Strategy Implementation

  • Develop comprehensive search syntax for multiple databases
  • Implement automated search translation across platforms
  • Execute iterative search validation with test sets

• Machine Learning-Assisted Screening

  • Train predictive models on pilot screening decisions
  • Implement active learning for study prioritization
  • Maintain human oversight for all inclusion decisions

• Structured Data Extraction

  • Design web-based extraction forms with structured fields
  • Implement dual independent extraction with reconciliation
  • Extract study design elements and health system assessments

• Knowledge Graph Population

  • Transform extracted data to graph model
  • Establish semantic relationships between entities
  • Implement ontology-based semantic reasoning

• Interactive Visualization Development

  • Create evidence gap maps using interactive visualization tools
  • Implement faceted search and filtering capabilities
  • Develop trend analysis and forecasting dashboards

Diagram 3: End-to-end SEM development workflow (Max Width: 760px)

The Scientist's Toolkit: Essential Research Reagents

Table 4: Research Reagent Solutions for SEM Implementation

Tool Category	Specific Solutions	Function	Implementation Considerations
Evidence Synthesis Platforms	DistillerSR, Rayyan, CADIMA	Manage systematic review process, screening, data extraction	Cloud-based collaboration, API access, compliance with PRISMA guidelines
Machine Learning Tools	SWIFT-Review, ASReview	Prioritize studies during screening, reduce manual workload	Training data requirements, model performance validation, human oversight
Graph Databases	Neo4j, Amazon Neptune, Azure Cosmos DB	Store and query connected EH data as knowledge graphs	Schema design, query performance, integration with semantic web technologies
FHIR Implementations	HAPI FHIR, Microsoft FHIR Server, Firely	Enable standards-based data exchange	Profile development, terminology service integration, security implementation
Terminology Services	Ontoserver, Snow Owl, BioPortal	Manage controlled vocabularies and ontologies	Mapping complexity, version management, multi-lingual support
Visualization Tools	Tableau, R Shiny, Python Dash	Create interactive evidence maps and dashboards	User experience design, performance with large datasets, accessibility compliance

Quality Assurance and Validation Framework

Data Quality and Methodological Rigor

Maintaining methodological rigor throughout the SEM development process is essential for producing regulatory-grade evidence maps. Quality assurance protocols should include:

• Dual independent review at critical decision points (study inclusion, data extraction)
• Machine learning validation against human decisions with continuous performance monitoring
• Data integrity checks through automated validation rules and outlier detection
• Reproducibility safeguards through version-controlled protocols and automated audit trails

Performance metrics should be established for each phase of the SEM development process, with target benchmarks for screening accuracy, data completeness, terminology consistency, and query response times. Regular interoperability testing with external systems, particularly EHDS2-connected infrastructure, ensures ongoing compliance with evolving regulatory requirements [65].

Regulatory Compliance and Documentation

Comprehensive documentation following established reporting guidelines (PRISMA, SEM-specific extensions) provides the transparency necessary for regulatory acceptance. Protocol registration in publicly accessible platforms enhances credibility and reduces duplication of effort across the research community. Specific attention should be paid to documenting:

• Legal basis for data processing under GDPR for secondary research use
• AI system conformity assessments under the AI Act for any machine learning components
• Security and privacy impact assessments for cross-border data transfers
• Data quality management systems and anomaly resolution procedures

The implementation of scalable, interoperable Systematic Evidence Maps represents a critical advancement in environmental health and chemical assessment research. By adopting knowledge graph technologies, standards-based interoperability frameworks, and automated workflow tools, researchers can overcome the challenges posed by heterogeneous, complex EH data. The protocols and methodologies detailed in this application note provide a foundation for constructing regulatory-grade evidence maps that support efficient evidence-based decision-making while complying with evolving regulatory landscapes including EHDS2 and AI Act requirements. As SEM methodology continues to evolve, ongoing attention to scalability, interoperability, and integration with emerging AI technologies will ensure these evidence products continue to deliver value across the chemical assessment and drug development lifecycle.

Resource Management Strategies for Efficient Evidence Synthesis

Within modern chemical assessment and drug development, the demand for robust, transparent, and timely evidence-based decision-making is paramount. Systematic reviews (SRs) have traditionally served as the gold standard for evidence synthesis; however, their utility is often restricted to narrowly focused questions and can be hampered by significant time and resource requirements [64]. In response to these challenges, Systematic Evidence Maps (SEMs) have emerged as a powerful tool for managing large bodies of evidence in a resource-efficient manner. SEMs are defined as databases of systematically gathered research that characterize broad features of an evidence base, making them uniquely suited for informing broader decision-making contexts in chemicals policy and risk management [64] [15]. This document provides detailed application notes and protocols for employing SEMs, framing them within a broader thesis on advancing chemical assessment research through efficient evidence synthesis.

Systematic Evidence Maps versus Other Review Methodologies

Selecting the appropriate evidence synthesis methodology is a critical first step in resource management. The choice depends on the research question, available resources, and desired output. The following table compares SEMs with other common review types to guide this selection.

Table 1: Comparison of Evidence Synthesis Methodologies Relevant to Chemical Assessment

Review Type	Description	Search	Critical Appraisal	Synthesis	Primary Purpose in Chemical Research
Systematic Evidence Map (SEM)	Systematically gathers and characterizes a broad body of research into a queryable database [64] [15].	Aims for exhaustive, comprehensive searching [69].	No formal quality assessment; focuses on characterizing evidence [69].	Graphical and tabular characterization of quantity and quality of literature [69].	Identify evidence clusters and gaps; inform priority-setting for risk assessment and future research [64].
Systematic Review (SR)	Seeks to systematically search for, appraise, and synthesize research evidence to answer a specific, focused question [70] [69].	Aims for exhaustive, comprehensive searching [70].	Quality assessment is required and may determine inclusion/exclusion [70].	Narrative with tabular accompaniment; may include meta-analysis [70].	Provide a definitive, quality-weighted answer to a specific question about chemical health risks [64].
Scoping Review	Preliminary assessment to identify the nature and extent of available research evidence [69].	Completeness determined by time/scope constraints; may include ongoing research [69].	No formal quality assessment [69].	Typically tabular with some narrative commentary [69].	Clarify conceptual boundaries and scope of a broad topic area in toxicology.
Literature (Narrative) Review	Generic term for an examination of recent or current literature, providing a summary without a strict systematic methodology [70].	May or may not include comprehensive searching [70].	May or may not include quality assessment [70].	Typically narrative [70].	Provide a general background or overview of a chemical or toxicological mechanism.
Meta-Analysis	A statistical technique used within a systematic review to combine the results of quantitative studies [71].	Aims for exhaustive searching as part of a systematic review [71].	Quality assessment may determine inclusion/exclusion and/or sensitivity analyses [71].	Graphical and tabular with narrative commentary; provides a quantitative consensus [71].	Statistically derive a more precise effect size for a specific health outcome from multiple chemical exposure studies.

SEMs are not intended to replace the deep evidence synthesis of a full systematic review but rather to act as a critical precursor. They provide an evidence-based approach to characterising the extent of available evidence, supporting trend analysis, and facilitating the identification of decision-critical information that warrants further analysis via SR [64]. This makes them exceptionally valuable for large-scale regulatory initiatives like EU REACH and US TSCA, where resource efficiency and transparency are crucial [15].

Protocol for Developing a Systematic Evidence Map in Chemical Assessment

The following section outlines a detailed, step-by-step protocol for constructing a Systematic Evidence Map, incorporating resource-efficient strategies at each stage.

Phase 1: Planning and Question Formulation

Objective: To define the scope of the SEM and establish a transparent, pre-defined protocol.

• Define the Broad Research Question: Unlike a systematic review, the question for an SEM is broader. For example: "What is the available evidence on in vitro and in vivo studies of the hepatotoxicity of Compound X?"
• Assemble the Review Team: The team should include at least three researchers with expertise in systematic methodology, subject matter (e.g., toxicology, chemistry), and information science [70].
• Develop and Publish a Protocol: A pre-publicated protocol minimizes resource waste from ad-hoc decisions. The protocol should define:
  • Eligibility Criteria (PICO/PECO): Define the Populations (or test systems), Interventions/Exposures (the chemical(s) of interest), Comparators, and Outcomes. While all outcomes may be considered, defining them a priori helps structure the map.
  • Information Sources: Specify the bibliographic databases (e.g., PubMed, Scopus, Embase), trial registries, and grey literature sources to be searched.
  • Search Strategy: Develop a comprehensive search strategy for at least one primary database, using controlled vocabulary (e.g., MeSH) and keywords. This strategy should be peer-reviewed, preferably by a librarian [70].
  • Data Extraction and Coding Fields: Pre-define the fields for the evidence database. These typically include study identifier, chemical tested, test system (e.g., cell line, species, sex), exposure regimen, measured endpoints, and study design features.

Phase 2: Evidence Collection and Screening

Objective: To systematically identify and screen all relevant evidence for inclusion in the map.

• Systematic Search Execution: Execute the search strategy across all specified databases and information sources. Document the dates and number of records retrieved from each source.
• Resource-Efficient Screening Management:
  • Use reference management software (e.g., EndNote, Zotero) to deduplicate records.
  • Employ a two-stage screening process (title/abstract, followed by full-text) against the eligibility criteria.
  • Leverage specialized systematic review software (e.g., Rayyan, Covidence) to manage the screening process efficiently, allowing for blind collaboration between reviewers and calculation of inter-rater reliability.
  • The goal is to be inclusive, capturing studies that characterize the evidence landscape, even if they would be excluded from an SR for poor quality.

Diagram 1: Evidence flow for systematic map creation.

Phase 3: Data Extraction and Coding

Objective: To populate the evidence database with standardized information from each included study.

• Develop a Data Extraction Form: Create a digital form within a spreadsheet or database software (e.g., Excel, Access) based on the pre-defined coding fields.
• Pilot the Form: Calibrate the extraction form and process using a small subset (e.g., 10-15) of included studies to ensure consistency and clarity among reviewers.
• Extract Data: A single reviewer can typically perform data extraction for an SEM, with a second reviewer verifying a subset for accuracy. The focus is on descriptive and methodological characteristics rather than quantitative outcome data.
  • Key Extraction Fields:
    • Reference Details: Author, year, journal.
    • Chemical & Exposure: Specific chemical(s) tested, dose/duration, route.
    • Test System: In vitro model (e.g., HepG2 cells) or in vivo model (e.g., rat, mouse).
    • Key Assays and Outcomes: Transcriptomic, proteomic, histopathological, clinical chemistry endpoints, etc.
    • Study Design: Controlled, dose-response, time-course, presence of controls.

Phase 4: Evidence Synthesis, Visualization, and Reporting

Objective: To analyze the coded database, generate visualizations that characterize the evidence base, and report findings.

• Database Queries: Use the structured database to run queries that identify evidence clusters (well-studied areas) and evidence gaps (under-studied areas). For example: "List all studies on Compound X that measured apoptosis in rodent liver."
• Data Visualization: Generate visual summaries to communicate the state of the evidence effectively. These may include:
  • Flow Diagrams: Illustrate the study identification and inclusion process (see Diagram 1).
  • Interactive Evidence Atlases: Web-based databases allowing users to filter and explore the evidence.
  • Structured Matrices/Heatmaps: Show the volume of evidence for different chemical-outcome or mechanism-outcome pairs (see Table 2).
• Reporting: The final report should describe the methods transparently, present the visualizations, and discuss the implications of the evidence landscape. This includes recommending areas for future primary research or targeted systematic reviews.

Table 2: Hypothetical Evidence Matrix for Hepatotoxicity of Compound X This matrix visualizes the volume of available evidence, helping to identify data-rich areas suitable for systematic review and data-poor areas representing research gaps.

Test System	Transcriptomic Alterations	Oxidative Stress	Apoptosis/Necrosis	Steatosis	Fibrosis
HepG2 Cell Line	15 studies	8 studies	5 studies	2 studies	0 studies
Primary Human Hepatocytes	5 studies	3 studies	2 studies	1 study	0 studies
Mouse (in vivo)	10 studies	12 studies	8 studies	6 studies	1 study
Rat (in vivo)	12 studies	15 studies	10 studies	9 studies	3 studies

Advanced Integration: SEMs, Adverse Outcome Pathways, and New Approach Methodologies

SEMs show strong potential in supporting the development and application of Adverse Outcome Pathways (AOPs) and New Approach Methodologies (NAMs) [72]. An AOP is a structured representation of biological events, starting from a Molecular Initiating Event (MIE) to an Adverse Outcome (AO) at the organism level. SEMs can be strategically deployed to systematically gather and map existing literature to an AOP framework.

Diagram 2: SEMs informing AOP development and assessment.

Protocol for Literature-Based AOP Development Using an SEM [72]:

• Define the AOP Scope: Identify the Molecular Initiating Event (MIE) and Adverse Outcome (AO) of interest (e.g., binding to receptor Y leading to liver fibrosis).
• Construct SEM with AOP-Specific Coding: Develop the SEM's data extraction fields to capture evidence for Key Events (KEs) and Key Event Relationships (KERs) in the hypothesized AOP.
• Map Evidence to the AOP Framework: Code each relevant study for the specific MIE, KEs, and AO it provides evidence for. This creates a clear linkage between individual studies and the structured biological pathway.
• Assess Certainty: Use the mapped evidence to inform assessments of the confidence in the AOP itself, potentially adapting frameworks like GRADE for evaluating mechanistic evidence [72].

This integration allows for a more transparent and evidence-based construction of AOPs, which are critical for leveraging NAMs in chemical risk assessment, ultimately reducing reliance on traditional animal studies.

The Scientist's Toolkit: Essential Reagents for Evidence Synthesis

Table 3: Key Research Reagent Solutions for Evidence Synthesis Projects

Tool/Resource	Function	Example Applications in SEM/SR
Reference Management Software (EndNote, Zotero)	Manages bibliographic data, and PDFs, and assists in deduplication.	Storing search results, removing duplicate records from multiple databases.
Systematic Review Platforms (Rayyan, Covidence, DistillerSR)	Web-based tools designed to manage the screening and data extraction phases of a review.	Facilitating blinded title/abstract and full-text screening by multiple reviewers; data extraction form creation and management.
Bibliographic Databases (PubMed, Scopus, Embase)	Primary sources for identifying published scientific literature.	Executing comprehensive, pre-defined search strategies to ensure all relevant evidence is captured.
Grey Literature Sources (ClinicalTrials.gov, EPA reports, ECHA database)	Sources of unpublished or non-commercially published information.	Identifying ongoing studies, regulatory data, and other evidence not found in traditional journals, reducing publication bias.
Data Visualization Software (Tableau, R/ggplot2, Python/Matplotlib)	Creates sophisticated static and interactive visualizations.	Generating evidence heatmaps (like Table 2), interactive flowcharts, and other graphs to characterize the evidence base.
Grading of Recommendations Assessment, Development and Evaluation (GRADE) Framework	A structured methodology for assessing the certainty of a body of evidence.	Assessing confidence in Key Event Relationships (KERs) within an AOP developed from the SEM [72].
Text Mining & Machine Learning Tools (NCBI's PubTator, custom NLP scripts)	Automates the identification of key concepts and relationships in large volumes of text.	Accelerating the initial screening phase or automatically extracting specific entities (e.g., chemical names, endpoints) during data extraction [72].

Leveraging Ontologies for Long-Term Data Utility and Connectivity

In the context of systematic evidence maps for chemical assessment research, ontologies serve as formal, machine-readable frameworks that explicitly define concepts, relationships, and rules within a domain [73]. They provide a standardized approach to representing complex data, enabling precise semantic meaning and logical inference that traditional data structures cannot capture [74]. For researchers, scientists, and drug development professionals, ontologies address critical challenges in data integration, interoperability, and long-term knowledge preservation, particularly when aggregating evidence across multiple studies, methodologies, and data sources.

The fundamental role of ontologies is to create a unified semantic layer that allows both humans and machines to interpret data consistently over time. This is achieved through explicit specification of conceptualizations, where entities are defined with precise relationships and logical constraints [75]. In chemical assessment research, this capability is paramount for maintaining data utility as analytical techniques evolve and regulatory requirements change. The application of ontologies ensures that data collected today remains discoverable, interpretable, and reusable for future evidence synthesis and meta-analyses.

The Role of Ontologies in Evidence Mapping for Chemical Assessment

Addressing Semantic Heterogeneity in Chemical Data

Chemical assessment research generates data characterized by multiple types of heterogeneity: syntactic (differences in representation format), structural (variations in data models), and most critically, semantic heterogeneity (differences in data interpretation) [76]. Ontologies specifically address semantic heterogeneity by providing unambiguous identification of entities and their relationships across heterogeneous information systems [76]. In systematic evidence mapping, this enables accurate cross-study comparisons and reliable evidence synthesis.

For drug development professionals, ontologies facilitate content explication by making explicit the definitions of terms and relationships, serving as a global query model for formulating complex research questions across distributed datasets, and providing verification mechanisms to validate data mappings and integration logic [76]. This tripartite functionality ensures that evidence maps maintain semantic consistency even as new studies are incorporated over time.

Enhancing FAIR Data Principles Compliance

Ontologies are foundational enablers of the FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable) in chemical research [74] [77]. By providing machine-readable semantic context, ontologies transform research data from mere collections of numbers and observations into meaningful, actionable knowledge assets. The NFDI4Chem initiative emphasizes that FAIR data is fundamentally about creating both human and machine-readable data, with ontologies being essential building blocks for achieving this dual capability [77].

In practical terms, ontological annotation makes data findable through precise semantic tagging, accessible through standardized query interfaces, interoperable through shared conceptual frameworks, and reusable through explicit documentation of experimental context and meaning [73]. This comprehensive FAIR alignment is particularly valuable for systematic evidence maps in chemical assessment, where long-term utility depends on maintaining these characteristics through multiple research cycles and technological changes.

Application Notes: Implementing Ontologies in Chemical Research

OntoSpecies: A Case Study in Chemical Data Integration

The OntoSpecies ontology represents a comprehensive implementation specifically designed for chemical species data management [74]. This ontology serves as a core component of The World Avatar knowledge graph chemistry domain and includes extensive coverage of chemical identifiers, physical and chemical properties, classifications, applications, and spectral information [74]. The implementation demonstrates how ontologies can integrate disparate chemical data sources into a unified semantic framework.

Key features of OntoSpecies include:

• Provenance and attribution metadata for ensuring data reliability and traceability
• Comprehensive property coverage including spectral data and chemical classifications
• SPARQL endpoint access enabling complex semantic queries across integrated data
• Dynamic population through software agents that harvest data from authoritative sources like PubChem and ChEBI [74]

For chemical assessment researchers, this approach enables novel types of problemsolving, such as identifying compounds with specific property combinations or predicting chemical behavior through semantic reasoning rather than simple data retrieval.

Architecture Approaches for Ontology-Based Integration

Table 1: Ontology Integration Architectures for Chemical Data

Approach	Description	Use Cases	Examples
Single Ontology	Uses one global reference model for integration	Homogeneous domains with standardized terminology	SIMS, Research Cyc [76]
Multiple Ontologies	Independent ontologies for each source with mappings between them	Integrating pre-existing, heterogeneous data sources	OBSERVER system [76]
Hybrid Approach	Multiple ontologies subscribing to a common top-level vocabulary	Evolving systems with specialized subdomains	Many OBO Foundry ontologies [76] [73]

The hybrid approach has gained significant traction in chemical assessment research because it balances domain specificity with interoperability. In this model, specialized chemical ontologies (e.g., ChEBI for chemical entities, CHMO for chemical methods) align with upper-level ontologies like BFO (Basic Formal Ontology) and share relationship definitions from RO (Relation Ontology) [73]. This approach allows researchers to maintain domain-specific conceptualizations while ensuring cross-domain query capability.

Experimental Protocols for Ontology Deployment

Protocol: Ontology-Enabled Systematic Evidence Mapping

Objective: Implement an ontology-driven framework for creating and maintaining systematic evidence maps in chemical assessment research.

Materials and Reagents:

• Semantic Workbench: Protégé ontology editor or similar tool for ontology management
• Triple Store: Graph database with SPARQL endpoint (e.g., Apache Jena Fuseki, Stardog)
• Mapping Tools: XML/RDF transformation pipelines or custom software agents
• Query Interfaces: SPARQL query engine and user-friendly frontend applications

Procedure:

• Domain Analysis and Ontology Selection

  • Identify core concepts in the evidence mapping domain (e.g., chemical compounds, assays, endpoints, study types)
  • Select appropriate established ontologies from the chemical ontology landscape (refer to Table 2)
  • Map relationships between concepts using standard relation ontologies (e.g., OBO Relation Ontology)

• Ontology Alignment and Extension

  • Align selected domain ontologies with upper-level ontologies (BFO preferred for OBO-compliant ontologies)
  • Create cross-references between overlapping concepts using owl:equivalentClass and owl:equivalentProperty
  • Develop specialized extension modules for domain-specific concepts not covered by existing ontologies

• Data Annotation Pipeline

  • Implement automated annotation of existing datasets using ontology terms
  • Establish quality control checks for annotation consistency and completeness
  • Develop transformation scripts to convert native data formats to RDF using ontology-based templates

• Query and Reasoning Infrastructure

  • Deploy ontology reasoner (e.g., HermiT, Pellet) to infer implicit knowledge
  • Implement SPARQL query templates for common evidence mapping questions
  • Create user interfaces that leverage ontological reasoning for evidence discovery

• Maintenance and Evolution

  • Establish versioning protocols for ontology updates
  • Implement change propagation mechanisms to maintain data-ontology alignment
  • Schedule periodic reviews against emerging standards and community practices

Protocol: Chemical Data FAIRification Workflow

Objective: Transform traditional chemical research data into FAIR-compliant semantic data using ontological annotation.

Procedure:

• Data Source Identification

  • Catalog available data sources (experimental results, literature extracts, regulatory documents)
  • Assess data quality and metadata completeness for each source
  • Prioritize sources based on evidence mapping objectives and data richness

• Ontology Mapping Specification

  • Create precise mapping documents linking source data fields to ontology classes and properties
  • Define transformation rules for data value standardization and unit conversion
  • Specify provenance metadata requirements using provenance ontologies (e.g., PROV-O)

• RDF Generation and Validation

  • Execute data transformation to generate RDF triples following mapping specifications
  • Validate output against ontological constraints using SHACL or SPARQL constraints
  • Resolve inconsistencies through iterative refinement of mapping rules

• Knowledge Graph Population

  • Load validated RDF into triple store with appropriate indexing strategies
  • Execute reasoner to materialize inferred knowledge
  • Perform completeness checks against source data metrics

• Application Integration

  • Develop specialized SPARQL queries for evidence mapping use cases
  • Implement API endpoints for programmatic access to semantic data
  • Create user interfaces that leverage ontological reasoning for evidence discovery

Visualization: Ontology Workflows in Chemical Research

Diagram 1: Ontology Data Integration Workflow

Diagram 2: Ontology Architecture for Evidence Mapping

Chemical Ontology Landscape

Table 2: Essential Ontologies for Chemical Assessment Research

Ontology	Scope	License	Key Classes & Properties	Use in Evidence Mapping
ChEBI [73]	Chemical entities of biological interest	CC-BY 4.0	Chemical substances, roles, structures	Chemical entity standardization
CHMO [73]	Chemical methods and techniques	CC-BY 4.0	Analytical methods, protocols	Experimental method annotation
OntoSpecies [74]	Comprehensive chemical properties	Custom OA	Identifiers, properties, spectra	Chemical property integration
BFO [73]	Upper-level ontology	CC-BY 4.0	Universal classes (entity, process)	Cross-domain interoperability
OBI [73]	Biomedical investigations	CC-BY 4.0	Assays, instruments, objectives	Study design annotation
IAO [73]	Information artifacts	CC-BY 4.0	Data items, documents	Evidence provenance tracking
PATO [73]	Phenotypic qualities	CC-BY 3.0	Qualities, characteristics	Endpoint standardization
UO [73]	Measurement units	CC-BY 4.0	SI and derived units	Unit consistency across studies

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Ontology Implementation

Tool Category	Specific Solutions	Function	Application Context
Ontology Editors	Protégé, WebProtégé	Ontology development and maintenance	Creating domain extensions and mappings
Triple Stores	Apache Jena, Stardog, GraphDB	RDF storage and SPARQL query processing	Evidence knowledge graph implementation
Reasoning Engines	HermiT, Pellet, ELK	Logical inference and consistency checking	Deriving implicit knowledge from evidence
Mapping Tools	RMLMapper, XSLT	Transforming structured data to RDF	Converting existing datasets to semantic format
Workbenches	KNIME, Orange with semantic extensions	Visual workflow design with ontology support	Designing evidence processing pipelines
Query Interfaces	YASGUI, SPARQL Explorer	User-friendly SPARQL query formulation	Enabling domain expert access to evidence

Best Practices for Sustainable Ontology Implementation

Stakeholder-Centric Ontology Development

Successful ontology implementation begins with deep stakeholder engagement to ensure the resulting semantic framework addresses real-world evidence mapping needs [78]. This involves collaborative requirement gathering with researchers, systematic review authors, regulatory scientists, and data managers to capture the nuanced information relationships essential for chemical assessment. The practice of "seeing through stakeholders' eyes" ensures ontologies reflect actual research workflows rather than abstract data models, significantly enhancing long-term adoption and utility [78].

Scientific Rigor and Standards Compliance

Ontology development must be grounded in scientific principles and align with established domain standards to ensure logical consistency and interoperability [78]. In chemical assessment research, this means leveraging well-established ontologies like ChEBI and BFO rather than developing isolated models. This standards-based approach reduces ambiguity and enhances information system interoperability across the evidence lifecycle [78]. The alignment with upper ontologies like BFO further enables integration with complementary research domains, extending the utility of evidence maps beyond immediate chemical assessment applications.

Data-Informed Ontology Design

Incorporating real-world data from the outset ensures ontological frameworks are grounded in practical evidence mapping requirements rather than theoretical constructs [78]. This involves analyzing existing datasets, evidence synthesis reports, and data exchange patterns to identify essential concepts, relationships, and constraints. This empirical foundation makes the ontology more relevant and applicable to ongoing chemical assessment activities, increasing stakeholder adoption and long-term sustainability.

Implementation and Maintenance Protocols

Establishing robust governance and maintenance procedures is critical for sustaining ontology utility as evidence mapping requirements evolve [79]. This includes versioning strategies, change management processes, and community engagement mechanisms to ensure the ontological framework adapts to emerging research questions and methodological advances. Regular reviews against evolving standards and practical implementation feedback create a continuous improvement cycle that maintains ontological relevance throughout the evidence map lifecycle.

SEMs vs. Other Methods: Validating Effectiveness in Regulatory Science

In the field of chemical assessment research, the ability to navigate vast scientific literature is paramount. Systematic Evidence Maps (SEMs) and Systematic Reviews (SRs) represent two distinct methodologies for evidence synthesis, each with unique purposes, processes, and outputs. While both employ rigorous systematic approaches, they serve different functions in the research ecosystem. SEMs provide a broad overview of the research landscape, identifying the existence, distribution, and characteristics of available evidence, whereas SRs focus on obtaining definitive answers to specific research questions, typically regarding the effectiveness or safety of interventions or exposures. For researchers, scientists, and drug development professionals, understanding the strategic application of each method ensures appropriate use of resources and generates the most relevant evidence for decision-making processes in chemical risk assessment and regulatory submissions [80] [81].

Comparative Analysis: Systematic Evidence Maps versus Systematic Reviews

The following table delineates the core distinctions between these two methodologies, highlighting their unique contributions to evidence-based chemical assessment.

Table 1: Comparative Analysis of Systematic Evidence Maps and Systematic Reviews

Feature	Systematic Evidence Maps (SEMs)	Systematic Reviews (SRs)
Primary Purpose	To systematically catalog and map the available evidence, describing the breadth and nature of a research field [3] [81].	To comprehensively synthesize and analyze evidence to answer a specific, focused question, often about intervention effectiveness or safety [80] [3].
Research Question	Broad in scope, aiming to "map the landscape" of a topic [80] [81].	Narrow and specific, often formulated using PICO/PECO (Population, Intervention/Exposure, Comparator, Outcome) criteria [80].
Data Extraction	Categorizes high-level study characteristics (e.g., study design, population, exposure type, outcome measured) [80] [81].	Extracts detailed data on methods, results, and risk of bias to support a synthesized conclusion [80] [3].
Quality Appraisal (Risk of Bias)	Often optional. If conducted, it is used to characterize the evidence base rather than exclude studies [80].	Mandatory. Critical for interpreting findings and grading the overall strength of evidence [3].
Key Outputs	Interactive databases, evidence gap maps (EGMs), graphical charts, and reports highlighting evidence clusters and gaps [80] [3] [21].	A synthesized summary of findings (narrative, tabular, or statistical meta-analysis) with conclusions about effects [3] [82].
Role in Chemical Assessment	Ideal for initial chemical prioritization, scoping health outcomes for a substance, and planning future primary research or systematic reviews [83].	Used to support definitive hazard identification, dose-response assessment, and inform risk management decisions [10].

Methodological Protocols

Protocol for Conducting a Systematic Evidence Map in Chemical Assessment

The following protocol, summarized in Table 2, provides a standardized workflow for developing a Systematic Evidence Map, drawing from established methodologies in environmental and human health research [80] [83].

Table 2: Protocol for a Systematic Evidence Map

Step	Description	Application in Chemical Assessment
1. Define Scope & Question	Formulate a broad research question and objectives. Define PECO elements.	Example: "Map the available mechanistic evidence informing the health outcomes associated with Perfluorohexanesulfonic Acid (PFHxS)" [83].
2. Develop a Protocol	Create a detailed, publicly available protocol outlining the methodology, including search strategy and inclusion/exclusion criteria [80] [10].	The protocol ensures transparency and reproducibility, critical for regulatory acceptance [10].
3. Literature Search	Perform a comprehensive search across multiple bibliographic databases and grey literature sources [80].	Search databases like PubMed, Scopus, and TOXLINE using chemical names and broad health outcome terms.
4. Study Screening	Screen identified studies for relevance using pre-defined inclusion/exclusion criteria, typically in a two-stage process (title/abstract, then full-text) [80].	Use software like DistillerSR to manage the screening process for thousands of identified studies [83].
5. Data Extraction	Extract high-level data into a predefined coding framework.	Extract data on study design (e.g., in vitro, in silico), organism, system, endpoint, and health outcome category (e.g., hepatic, immune) [80] [83].
6. Data Coding & Categorization	Code and categorize the extracted data according to the framework.	Categories may include health effects like thyroid, immune, developmental, and hepatic toxicity [83].
7. Visual Presentation	Develop visualizations to present the mapped evidence, such as Evidence Gap Maps (EGMs).	Create interactive maps or heat maps showing the volume of evidence for different health outcomes, highlighting data-rich and data-poor areas [80] [21].

Protocol for Conducting a Systematic Review for Chemical Risk Evaluation

This protocol, used by agencies like the U.S. EPA for chemical risk evaluations under TSCA, involves a more in-depth process focused on deriving a quantitative or qualitative conclusion [10].

Table 3: Protocol for a Systematic Review in Chemical Risk Assessment

Step	Description	Application in Chemical Risk Evaluation
1. Formulate Specific Question	Define a focused question using PECO/PICO.	Example: "Does chronic oral exposure to substance X cause liver toxicity in rodents?"
2. Develop & Peer-Review Protocol	Draft a detailed protocol, often subject to peer review by advisory committees [10].	The EPA's TSCA protocol is reviewed by the Science Advisory Committee on Chemicals (SACC) [10].
3. Comprehensive Search	Conduct an exhaustive search with a highly sensitive strategy to capture all relevant evidence.	Similar to SEMs but may be more targeted to specific health outcomes and study designs suitable for risk assessment.
4. Study Screening	Screen studies against strict eligibility criteria.	Focus on identifying studies that report quantitative data on the specific exposure-outcome relationship.
5. Detailed Data Extraction	Extract detailed data on study methods, results, and potential confounding factors.	Extract specific data points such as dose, response, incidence, effect size, and statistical measures.
6. Risk of Bias Assessment	Critically appraise the internal validity of each study using a validated tool [10].	Use tools designed for animal toxicology or observational studies to evaluate confidence in each study's results.
7. Synthesis & Meta-Analysis	Synthesize findings narratively and, if appropriate, statistically via meta-analysis.	Combine results from studies to estimate an overall effect and explore heterogeneity.
8. Report & Conclude	Report findings with a conclusion on the strength of evidence for the health outcome.	Outputs directly inform the hazard identification and dose-response analysis in a risk assessment [10].

Application in Chemical Assessment: A Case Study on PFHxS

The U.S. Environmental Protection Agency (EPA) employed a Systematic Evidence Map to organize and evaluate the mechanistic data for Perfluorohexanesulfonic Acid (PFHxS). The goal was to identify the available evidence and pinpoint research needs linking PFHxS to specific health outcomes [83].

Experimental Workflow & Key Findings:

• Literature Identification: A systematic search identified 2,441 unique studies up to April 2023.
• Screening & Categorization: After screening, 235 studies were tagged as mechanistic and mapped to 11 pre-defined health effect categories from the IRIS PFHxS draft assessment (e.g., thyroid, immune, hepatic) [83].
• Evidence Mapping Result: The SEM revealed an uneven evidence base. Most studies focused on mechanisms of hepatotoxicity (45 studies) and developmental neurotoxicity (36 studies). In contrast, no mechanistic studies were identified for PFHxS-related cardiometabolic or renal health outcomes, clearly highlighting critical data gaps [83].

This SEM provided a rapid and clear "big picture" of the evidence, allowing researchers and regulators to see where evidence was concentrated and where it was absent. This directly informs priorities for future research to substantiate potential hazards identified by other evidence streams, such as epidemiology [83].

The Scientist's Toolkit: Essential Reagents & Materials

Table 4: Key Research Reagent Solutions for Evidence Synthesis

Reagent / Tool	Function in Evidence Synthesis
DistillerSR	A web-based, systematic review software used to manage the entire literature screening and data extraction process, ensuring compliance and reducing human error [83].
EPPI-Reviewer	A specialized software tool for managing and analyzing data in all forms of systematic review, including coding and classification for mapping reviews [80].
PECO/PICO Framework	A structured framework to define the Population, Exposure/Intervention, Comparator, and Outcome(s) of interest, forming the foundation of a focused research question [80] [83].
Evidence Gap Map (EGM)	A graphical representation (often interactive) that visually plots the relationships between interventions/exposures and outcomes, showing the volume and distribution of available evidence [80] [81].
Systematic Review Protocol	A detailed, a priori plan that defines the study's objectives and methods, crucial for minimizing bias and ensuring transparency and reproducibility [80] [10].

Systematic Evidence Maps and Systematic Reviews are complementary yet distinct tools in the chemical assessor's arsenal. The choice between them is not a matter of hierarchy but of strategic alignment with the research objective. SEMs are the optimal choice for scoping broad fields, identifying evidence clusters and gaps, and guiding research agendas. In contrast, SRs are indispensable for integrating detailed evidence to answer specific questions about chemical hazards and risks, thereby directly supporting regulatory decision-making. For a robust chemical assessment program, both methodologies are essential. Beginning with an SEM can efficiently scope the landscape and determine whether and where a full Systematic Review is warranted, ensuring that resources are allocated effectively to generate the most impactful evidence for protecting human health and the environment.

Comparative Analysis with Scoping Reviews and Rapid Review Methods

Within the domain of chemical assessment research, evidence synthesis serves as a cornerstone for informed decision-making. Systematic evidence maps provide a foundational overview of the research landscape, charting the extent and distribution of available evidence without aggregating results [84]. These maps function as crucial tools for identifying knowledge clusters and gaps, thereby informing future research agendas. Within this broader ecosystem, scoping reviews and rapid reviews represent two distinct but complementary methodological approaches to evidence synthesis. When a systematic evidence map reveals a sufficiently dense area of research, scoping reviews and rapid reviews offer pathways to delve deeper, each with specific applications, methodologies, and outputs tailored to different research objectives and time constraints. This article provides a detailed comparative analysis and protocols for these two review types, contextualized specifically for researchers, scientists, and professionals in drug development and chemical assessment.

Definition and Conceptual Frameworks

Scoping Reviews

A scoping review aims to map the key concepts, types of evidence, and research gaps on a particular topic [84]. Its primary purpose is to provide an overview of the existing literature regardless of the quality of the included studies. Scoping reviews are particularly valuable in chemical assessment for examining emerging evidence, clarifying working definitions for exposure metrics or outcome measures, and identifying the scope and nature of available research on a particular compound or class of compounds. They are characterized by broad questions and comprehensive search strategies and may not be accompanied by a formal quality assessment of the included studies.

Rapid Reviews

A rapid review is a form of knowledge synthesis in which components of the systematic review process are simplified or omitted to produce information in a timely manner [85]. These reviews are conducted to meet the urgent evidence needs of decision-makers, such as those in regulatory agencies or clinical development teams, who cannot wait for the lengthy timeline of a full systematic review. While numerous approaches exist, they consistently prioritize timeliness, with conduct times ranging from less than one month to twelve months, though many are completed between one and six months [85].

Comparative Analysis: Objectives, Scope, and Methodologies

The selection between a scoping review and a rapid review is dictated by the research question. The table below summarizes the core characteristics of each review type for direct comparison.

Table 1: Comparative Analysis of Scoping Reviews and Rapid Reviews

Characteristic	Scoping Review	Rapid Review
Primary Objective	To map key concepts, evidence types, and gaps in a broad field [84].	To provide timely evidence for decision-making by streamlining systematic review methods [85].
Research Question	Broad, exploratory (e.g., "What is the scope of research on the neurodevelopmental effects of chemical X?").	Focused, often on intervention efficacy or safety (e.g., "Is drug Y effective for condition Z?") [84].
Scope	Comprehensively covers a topic area, often with heterogeneous evidence.	Narrower, with boundaries set to expedite the process.
Search Strategy	Comprehensive, seeks to be extensive, multiple databases.	Streamlined (e.g., limited by date, fewer databases, no grey literature) [85].
Study Selection	Typically involves screening by two reviewers, but may be flexible.	Often streamlined (e.g., single screener with verification, or screen of excluded studies) [85].
Critical Appraisal	Usually not performed [84].	Often omitted or conducted by a single reviewer [85].
Data Synthesis	Presentation of a narrative summary, often with quantitative and qualitative categorization.	Primarily narrative summary; quantitative synthesis (meta-analysis) is rare [85].
Key Outputs	Conceptual map, evidence inventory, identification of research gaps.	Timely summary of evidence, often with conclusions on a specific question.
Timeframe	Can be lengthy due to broad scope and volume of evidence.	1 to 12 months, commonly 1-6 months [85].
Reporting Guideline	PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) [84].	No universal standard; often adapts PRISMA.

Detailed Application Notes and Protocols

Protocol for Conducting a Scoping Review

Scoping reviews are ideal for initial assessments of chemical exposures and health outcomes where the literature is diverse and not yet comprehensively cataloged.

Phase 1: Planning and Protocol Development

• Define the Objective and Question: Clearly articulate the review's purpose. Use the PCC (Population, Concept, Context) framework for question development, which is often more suitable than PICO for scoping reviews.
• Develop a Protocol: A pre-published protocol is recommended. It should detail the objectives, methods, and eligibility criteria.
• Eligibility Criteria: Define criteria for sources based on PCC. Consider all study designs and evidence types. Delineate boundaries by date, language, and context.

Phase 2: Searching and Selecting Evidence

• Search Strategy: Develop a comprehensive search strategy with a librarian or information specialist. Search multiple electronic databases (e.g., PubMed, Embase, TOXLINE). Iterative searching, including scanning reference lists, is a key feature.
• Study Selection: Use a two-stage process (title/abstract, then full-text). At least two reviewers should independently screen studies, with a process for resolving disagreements.

Phase 3: Data Extraction and Charting

• Data Charting Form: Develop a standardized form to extract data from included studies. This is not about aggregating results but about cataloging characteristics.
• Variables to Extract: Typically includes study details (author, year), population characteristics, concept (exposure/intervention), context, key findings relevant to the map, and research gaps noted.
• Process: The form is piloted and refined. Data extraction is often performed by a single reviewer and verified by a second.

Phase 4: Analysis and Presentation of Results

• Analysis: Use quantitative (e.g., counts of study types) and qualitative (e.g., thematic analysis) methods to present an overview of the evidence.
• Presentation: The results are presented in a narrative summary, accompanied by diagrams, tables, and charts that map the evidence. A common output is an evidence table summarizing all included studies.

The workflow for this protocol is illustrated in the following diagram:

Protocol for Conducting a Rapid Review

Rapid reviews are suited for urgent questions in drug development, such as a preliminary safety assessment of an excipient or a comparative efficacy review for a grant application.

Phase 1: Pragmatic Scoping and Streamlining

• Consult Stakeholders: Engage decision-makers to define the core question and key outcomes. Establish a firm deadline.
• Define Streamlining Methods: A priori, decide which systematic review steps will be simplified. Common choices are listed in Table 2.
• Develop a Protocol: Document all planned streamlining decisions in a protocol to ensure transparency.

Phase 2: Targeted Evidence Retrieval

• Search Strategy: Limit the search by date (e.g., last 10 years), language (e.g., English only), and number of databases. Grey literature searching may be omitted or limited to key sources.
• Study Selection: Use a single reviewer for title/abstract screening, with a second reviewer verifying a subset of excluded studies. Full-text screening is ideally done by two reviewers independently.

Phase 3: Focused Data Extraction and Appraisal

• Data Extraction: Use a simplified data extraction form focusing on the PICO elements and primary outcomes. This can be done by one reviewer with verification by a second.
• Risk of Bias Appraisal: This step is often omitted or conducted by a single reviewer using a streamlined tool [85].

Phase 4: Expedited Synthesis and Reporting

• Synthesis: A narrative summary is the most common form of synthesis. If studies are sufficiently homogeneous, a meta-analysis may be performed, but this is not the norm.
• Reporting: Clearly report all streamlining methods and potential biases introduced by them. Conclusions should be framed with these limitations in mind.

The following diagram outlines the rapid review workflow with key streamlining decision points:

Table 2: Common Streamlining Methods in Rapid Reviews (based on [85])

Methodological Step	Common Streamlining Approach	Reported Frequency in Literature
Literature Search	Limit by date (e.g., last 5-10 years)	68%
	Limit by language (e.g., English only)	49%
	Search published literature only (no grey literature)	24%
	Search only one database	2%
Study Selection	Single reviewer screen, with verification of excluded studies	6%
Data Extraction	Single reviewer extract, with second reviewer verification	23%
Quality Appraisal	Omit risk of bias/quality appraisal	7%
	Single reviewer conducts quality appraisal	7%
Data Synthesis	Present results as a narrative summary (no meta-analysis)	78%

The Scientist's Toolkit: Essential Reagents for Review Methodology

Executing a robust review requires a suite of "methodological reagents" – standardized tools and resources that ensure rigor, reproducibility, and efficiency.

Table 3: Key Research Reagent Solutions for Evidence Synthesis

Tool/Resource	Function	Application Notes
PRISMA-ScR Checklist	Reporting guideline for scoping reviews. Ensures transparent and complete reporting of the review process [84].	Essential for manuscript preparation and peer review.
PCC Framework	(Population, Concept, Context) used to define the scope and question for a scoping review.	Provides a more flexible alternative to the PICO framework for broad questions.
Rayyan / Covidence	Web-based tools for managing the study screening and selection process.	Facilitates blinding of reviewers, conflict resolution, and progress tracking.
Systematic Review Repository	Platforms like PROSPERO for pre-registering review protocols.	Reduces duplication of effort and mitigates reporting bias. Mandatory for many funders.
JBI Sumari	Software platform supporting the entire systematic review process, including scoping and rapid reviews.	Supports development of protocols, data extraction, critical appraisal, and synthesis.
Automated Screening Tools	AI-based tools (e.g., ASReview, RobotAnalyst) that prioritize records during title/abstract screening.	Particularly valuable in rapid reviews and scoping reviews with large search yields to reduce screening workload.
Data Visualization Software	Tools like Tableau, R/ggplot2, or even PowerPoint to create evidence maps and summary figures.	Critical for translating the results of a scoping review into an accessible format for stakeholders.

Scoping reviews and rapid reviews are powerful, distinct tools within the evidence synthesis toolkit for chemical assessment and drug development. Scoping reviews provide the broad, conceptual map needed to understand a sprawling research landscape, while rapid reviews deliver timely, focused evidence to meet pressing decision-making deadlines. The choice between them is not one of superiority but of strategic alignment with the research objective, available resources, and intended application. By adhering to the structured protocols and utilizing the essential tools outlined in this article, researchers can rigorously apply these methodologies to generate impactful, reliable evidence to advance the field.

In the face of expanding chemical inventories and limitations of traditional animal testing, regulatory toxicology increasingly relies on New Approach Methodologies (NAMs) to fill critical data gaps. Systematic evidence maps play a pivotal role in organizing existing knowledge and identifying priority areas for assessment [12]. This application note demonstrates the practical implementation of expert-driven read-across, a prominent NAM, through a case study adapted from US EPA practices. The documented protocol provides a framework for using structural, toxicokinetic, and toxicodynamic similarity to derive screening-level risk values for data-poor substances, leveraging existing assessments from robust programs like the EPA's Integrated Risk Information System (IRIS) and ATSDR's Toxicological Profiles.

Experimental Protocol: Expert-Driven Read-Across for Chemical Hazard Assessment

Scope and Application

This protocol provides a standardized methodology for identifying a suitable source analogue and applying its point of departure (POD) to a target data-poor chemical via quantitative read-across. It is applicable within human health risk assessment for environmental contaminants where oral toxicity data are insufficient, framed within the context of systematic evidence mapping for chemical assessment [12] [86].

Terminology and Definitions

• Read-across: A technique for predicting a property of a target chemical by using data from the same property from analogous chemicals (source analogues).
• Point of Departure (POD): A dose (e.g., NOAEL, BMD) that marks the beginning of a low-dose extrapolation.
• Toxicokinetics (TK): The study of how a chemical enters, moves through, and exits the body.
• Toxicodynamics (TD): The study of the molecular and cellular mechanisms of action of a chemical.
• Systematic Evidence Map: A structured and transparent collection and categorization of evidence to identify existing knowledge and gaps [12].

Pre-Experimental Requirements

• Personnel Competency: Personnel must have training in toxicological principles, structural chemistry, and use of the required databases.
• Health and Safety: Consult institutional Environmental Health and Safety (EH&S) guidelines for procedures regarding safe handling of chemical data and any physical chemical substances [87].
• Data Integrity: All data sources, similarity justifications, and decisions must be documented to ensure transparency and reproducibility [18] [88].

Materials and Equipment

Research Reagent Solutions & Essential Materials

Table 1: Essential Research Materials and Databases

Item Name	Function/Application	Specifications/Requirements
ChemIDplus Database	Identifies structurally similar chemicals based on fingerprint and Tanimoto coefficient.	Similarity threshold typically ≥ 50%; National Library of Medicine resource [86].
DSSTox Database	Identifies structurally similar chemicals with existing risk assessment data.	Use "IRIS_v1b" search option; U.S. EPA resource [86].
ToxCast/Tox21 Database	Provides high-throughput screening (HTS) bioactivity data for evaluating toxicodynamic similarity.	Contains >1,800 chemicals screened in >700 assay endpoints; U.S. EPA resource [86].
IRIS and PPRTV Databases	Sources for verified toxicity data and Points of Departure (PODs) for candidate analogues.	U.S. EPA's official toxicity value databases [86].
ATSDR Toxicological Profiles	Sources for verified toxicity data and Minimal Risk Levels (MRLs) for candidate analogues.	Profiles provide comprehensive toxicological summaries [86].

Step-by-Step Procedure

Step 1: Identification of Potential Analogues

• Input the target chemical (e.g., p,p'-DDD) into the ChemIDplus database.
• Execute a similarity search using the predefined threshold (≥ 50% Tanimoto similarity). Record all resulting chemicals.
• Cross-reference the list from Step 2 with the IRIS and ATSDR databases to retain only those analogues with existing oral non-cancer health reference values (e.g., Reference Doses, Minimal Risk Levels).
• Repeat the search process using the DSSTox database, applying the same similarity threshold and filtering for chemicals with IRIS assessments.
• Combine and deduplicate the lists from ChemIDplus and DSSTox to create a final candidate list of analogues [86].

Step 2: Multi-Dimensional Similarity Assessment

Evaluate the target and each candidate analogue across three primary similarity contexts. Document all evidence and justifications.

• Structural & Physicochemical Similarity:

  • Compare fundamental molecular structures and properties (e.g., functional groups, molecular weight, log P).
  • Justify that the compounds form a valid chemical category.

• Toxicokinetic (TK) Similarity:

  • Gather and compare data on absorption, distribution, metabolism, and excretion (ADME).
  • Give particular weight to metabolic pathway similarity. For p,p'-DDD, shared metabolic pathways with p,p'-DDT (e.g., dehydrochlorination to p,p'-DDE) provided a strong justification for read-across [86].

• Toxicodynamic (TD) Similarity:

  • Compare mechanisms of action (MOA) and adverse outcome pathways (AOPs).
  • Utilize in vitro HTS data from ToxCast to compare bioactivity profiles and potency across multiple assay endpoints. This provides empirical evidence for toxicodynamic similarity [86].

Step 3: Source Analogue Selection and POD Application

• Integrate evidence from all three similarity contexts (Structure, TK, TD) to select the single best source analogue.
• Justify the selection by explaining which similarity context was most decisive. In the p,p'-DDD case, toxicokinetics was instrumental in selecting p,p'-DDT over other candidates [86].
• Apply the POD (e.g., NOAEL) from the source analogue to the target chemical.
• Account for uncertainty in the read-across prediction. The use of a closely related analogue with extensive data may reduce uncertainty, but this must be explicitly stated.

Step 4: Reporting and Documentation

For reproducibility and regulatory acceptance, report the following in a structured format [18] [88]:

• Rationale for the read-across.
• Identification and characterization of the target chemical and all candidate analogues.
• Data for each similarity context and a weight-of-evidence conclusion.
• Final source analogue and its POD, with a clear justification for its selection.
• Quantitative read-across prediction and an assessment of associated uncertainties.

Troubleshooting

• Problem: No structurally similar compounds with existing health guidance values are found.
  • Solution: Systematically lower the Tanimoto similarity threshold in incremental steps (e.g., to 40%) and re-run the search [86].
• Problem: Conflicting evidence between similarity contexts (e.g., structural similarity is high, but toxicodynamic profiles are divergent).
  • Solution: Do not proceed with the read-across. The weight of evidence does not support a reliable prediction. Seek an alternative analogue or assessment method.

The following tables summarize the types of data collected and compared during the read-across process for the p,p'-DDD case study.

Table 2: Analogue Candidate List with Structural and Toxicological Data

Chemical Name	Tanimoto Similarity to p,p'-DDD	Available Oral POD (mg/kg-day)	Critical Effect	Metabolic Pathway Similarity
p,p'-DDT (Source Analogue)	>80%	NOAEL = 0.05	Liver hypertrophy, neurotoxicity	High (e.g., dehydrochlorination to DDE)
p,p'-DDE	>80%	NOAEL = 0.05 (ATSDR)	Liver effects, developmental	Moderate
Methoxychlor	~70%	NOAEL = 5.0 (PPRTV)	Liver, kidney, ovarian effects	Low (different primary metabolism)

Table 3: ToxCast Bioactivity Profile Comparison (Illustrative Data)

Assay Endpoint / Target	p,p'-DDD Activity (AC50, µM)	p,p'-DDT Activity (AC50, µM)	p,p'-DDE Activity (AC50, µM)	Similarity Inference
ERα Agonism	2.5	1.8	0.9	High (comparable potency)
AR Antagonism	5.1	3.5	15.0	Moderate (DDT & DDD more similar)
Thyroid Receptor Agonism	Inactive	Inactive	Inactive	High (same lack of effect)

Workflow and Relationship Visualizations

This application note details a robust protocol for implementing expert-driven read-across, demonstrating how systematic methodologies and NAMs can be validated through application in a regulatory context. The integration of systematic evidence mapping with quantitative read-across provides a transparent and defensible approach to addressing data gaps for chemicals lacking full toxicological profiles. The case study on p,p'-DDD illustrates the critical role of toxicokinetic and high-throughput screening data in building scientific confidence for the use of read-across in generating screening-level health reference values, thereby supporting the mission of programs like EPA IRIS and ATSDR to protect public health.

Systematic Evidence Maps (SEMs) have emerged as a critical tool in evidence-based toxicology and chemical risk assessment, offering a solution to the challenge of navigating large and complex evidence bases [66] [64]. While systematic reviews provide deep synthesis of narrowly focused questions, SEMs provide a broader overview of research landscapes, characterizing the extent, distribution, and gaps in available evidence [81]. This methodology is particularly valuable for chemicals policy and risk management workflows where decision-makers face broad information needs that cannot be addressed by single systematic reviews [64]. The analysis of 39 published evidence maps provides a robust foundation for establishing methodological best practices, ensuring that SEMs produced for chemical assessment research are rigorous, transparent, and fit-for-purpose.

Quantitative Analysis of Published Evidence Maps

Characteristics of Evidence Maps from Systematic Review

A systematic review of 39 published evidence maps revealed significant diversity in how the methodology was applied and described across different research contexts [89]. This analysis provides crucial empirical data to inform best practices in chemical assessment research.

Table 1: Characteristics of 39 Published Evidence Maps

Characteristic	Findings	Frequency
Stated Purpose	Identification of research gaps	67% (31/39)
	Stakeholder engagement process or user-friendly product	58% (31/39)
Methodological Approach	Explicitly systematic approach	100% (39/39)
Map Presentation Format	Figure or table explicitly called "evidence map"	67% (26/39)
	Online database as the evidence map	21% (8/39)
	Mapping methodology without visual depiction	13% (5/39)
Geographical Origin	United States	49% (19/39)
	Australia	26% (10/39)
	United Kingdom	Remaining publications

The analysis found that all evidence maps explicitly used a systematic approach to evidence synthesis, indicating consensus on the fundamental requirement for methodological rigor [89]. However, heterogeneity was observed in how evidence maps were presented and formatted, suggesting ongoing evolution in how the methodology is communicated to end-users.

Comparative Analysis of Evidence Synthesis Methodologies

Understanding how SEMs complement other evidence synthesis methodologies is essential for appropriate application in chemical assessment research.

Table 2: Comparison of Evidence Synthesis Methodologies for Chemical Assessment

Methodology	Systematic Review	Systematic Evidence Map	Scoping Review
Primary Purpose	Answer specific focused question through synthesis	Identify, characterize, and organize broad evidence base	Explore key concepts and evidence types in a field
Scope	Narrow and specific	Broad	Broad or exploratory
Time/Resources	High	Medium to High	Variable
Output	Quantitative or qualitative synthesis	Queryable database, visualizations, gap analysis	Descriptive narrative, conceptual framework
Ideal Use Case	Determining chemical safety for specific health endpoint	Priority-setting for chemical assessment programs	Understanding conceptual boundaries of research area

SEMs occupy a distinct space in the evidence synthesis ecosystem, particularly valuable for "forward looking predictions or trendspotting in the chemical risk sciences" and as "a critical precursor to efficient deployment of high quality systematic review methods" [64].

Experimental Protocols for Systematic Evidence Mapping

Core Workflow for Evidence Mapping

The following protocol provides a detailed methodology for conducting SEMs specific to chemical assessment research, synthesized from analyzed publications and methodological guidance.

Systematic Evidence Mapping Workflow for Chemical Assessment

Problem Formulation and Scope Definition

• Define evidence map objectives: Clearly articulate the chemical classes, health outcomes, exposure scenarios, and populations of interest
• Engage stakeholders: Consult risk assessors, regulators, and subject matter experts to ensure relevance to decision contexts [89]
• Develop a priori protocol: Document search strategy, inclusion/exclusion criteria, and data extraction framework before commencing

Systematic Search Strategy

• Comprehensive source selection: Search multiple bibliographic databases (e.g., PubMed, Web of Science, Embase, TOXLINE) relevant to toxicology and environmental health [90]
• Chemical-specific search terms: Utilize CAS numbers, chemical names, synonyms, and related terminology from controlled vocabularies
• Search period documentation: Clearly report date ranges covered and last search date for reproducibility

Dual Screening Process

• Title/abstract screening: Two independent reviewers apply predefined inclusion/exclusion criteria [1]
• Full-text assessment: Dual review of potentially relevant studies with conflict resolution process
• Transparency documentation: Record number of studies excluded at each stage with reasons for exclusion

Data Extraction and Knowledge Structuring Protocol

Knowledge Graph Data Structure for Evidence Maps

Standardized Data Extraction

• Study metadata: Author, year, journal, funding source, organizational affiliations
• Chemical characterization: Specific substances, chemical classes, CAS numbers, physicochemical properties
• Methodological details: Study type (in vitro, in vivo, epidemiological), model systems, exposure duration and routes, endpoints measured
• Outcome data: Direction and significance of effects, dose-response relationships, points of departure where available

Controlled Vocabulary Coding

• Code development: Create standardized terminology for chemical classes, toxicological endpoints, and study methodologies
• Coding process: Assign controlled vocabulary labels to extracted data to enable meaningful comparisons despite heterogeneity [66]
• Document coding rules: Maintain detailed codebook with definitions and application examples

Knowledge Graph Implementation

• Flexible data structure: Utilize schemaless or graph-based approaches rather than rigid, flat tables to accommodate highly connected, heterogeneous EH data [66]
• Entity-relationship modeling: Structure data as interconnected entities (chemicals, studies, outcomes) rather than isolated records
• Semantic integration: Leverage existing ontologies (e.g., CHEBI, OBO Foundry resources) where possible to enhance interoperability

The Researcher's Toolkit for Evidence Mapping

Essential Research Reagent Solutions for Evidence Mapping

Table 3: Essential Tools and Resources for Chemical Evidence Mapping

Tool Category	Specific Solutions	Function in Evidence Mapping
Bibliographic Databases	PubMed, Web of Science, Embase, TOXLINE	Comprehensive identification of toxicological literature
Chemical Registries	CAS Registry, DSSTox, CompTox Chemicals Dashboard	Chemical identification, standardization, and classification
Data Extraction Tools	Systematic Review Assistant (SRA), CADIMA, DistillerSR	Streamline screening and data extraction processes
Data Storage Technologies	Graph databases (Neo4j), SQL databases, Spreadsheets	Structured storage of extracted evidence with query capabilities
Controlled Vocabularies	CHEBI, MeSH, OBO Foundry ontologies	Standardized coding of chemical, biological, and methodological concepts
Visualization Platforms	Tableau, R Shiny, EPPI-Mapper	Create interactive evidence maps and gap visualizations

Application in Chemical Risk Assessment Contexts

The systematic analysis of 39 evidence maps reveals several critical applications in chemical assessment research. SEMs provide an evidence-based approach to characterizing available evidence and supporting "forward looking predictions or trendspotting in the chemical risk sciences" [64]. They facilitate identification of related bodies of decision-critical chemical risk information that could be further analyzed using systematic review methods and highlight evidence gaps that could be addressed with additional primary studies.

In practice, evidence maps have been successfully applied to address specific chemical assessment challenges. For example, a recent evidence map on human toxicodynamic variability identified 23 in vitro studies providing quantitative estimates of variability factors, revealing significant data scarcity and heterogeneity that complicates chemical risk assessment [90]. This application demonstrates how SEMs can characterize the nature and extent of available evidence on specific toxicological questions relevant to refining uncertainty factors and chemical-specific assessment factors.

The transition toward knowledge graphs as a flexible, schemaless, and scalable model for systematically mapping environmental health literature represents a significant methodological advancement [66]. This approach is particularly well-suited to the long-term goals of systematic mapping methodology in promoting resource-efficient access to the wider environmental health evidence base, with several graph storage implementations readily available and proven use cases in other fields.

The analysis of 39 published evidence maps provides a robust foundation for establishing best practices in chemical assessment research. Successful implementation requires careful attention to problem formulation, systematic search strategies, dual screening processes, standardized data extraction with controlled vocabulary coding, and flexible knowledge structures that can accommodate the highly connected nature of toxicological evidence. By adopting these evidence-based methodologies, researchers and chemical assessment professionals can produce SEMs that effectively support priority-setting, trend identification, and resource-efficient evidence-based decision-making in chemicals policy and risk management.

Modern chemical regulations like the EU's REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) and the U.S. TSCA (Toxic Substances Control Act) face a formidable challenge: efficiently evaluating thousands of chemicals using the best available science without being overwhelmed by the exponentially growing volume of research data [4]. Regulatory bodies need to make timely, transparent, and evidence-based decisions on chemical safety, often with limited resources [4] [64]. Systematic Evidence Maps (SEMs) have emerged as a powerful methodology to address this challenge by providing a structured, comprehensive, and queryable overview of broad evidence bases [4] [64] [66]. Unlike systematic reviews, which synthesize evidence to answer a specific, narrow question, SEMs characterize the extent and nature of available research, highlighting evidence clusters and critical gaps [4] [66]. This application note details how SEMs function as critical tools for enhancing the resource efficiency, transparency, and effectiveness of REACH and TSCA regulatory initiatives.

Systematic Evidence Maps: Purpose and Methodology

Defining Systematic Evidence Maps

A Systematic Evidence Map (SEM) is a queryable database of systematically gathered research evidence, developed following a rigorous methodology to minimize bias and maximize transparency [4] [66]. Their primary purpose in regulatory science is to organize and characterize a large body of policy-relevant research, enabling trend analysis, priority-setting, and informed decision-making [4] [64]. They are not a substitute for the detailed evidence synthesis of a systematic review but serve as a critical precursor, ensuring that subsequent deep-dive analyses are targeted efficiently [4]. SEMs provide a foundational resource for evidence-based problem formulation, allowing regulators to ask: "What is the scope of the existing evidence?" and "Where should limited assessment resources be focused?" [20].

Standardized Protocol for SEM Development

The development of a robust SEM follows a structured, protocol-driven process. The U.S. EPA has established a template for creating SEMs for its Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) programs [20]. The workflow can be visualized as follows:

The key methodological steps are:

• Problem Formulation and PECO Criteria: The process begins with defining the Populations, Exposures, Comparators, and Outcomes (PECO) criteria. For broad evidence mapping, the PECO is kept intentionally wide to capture a comprehensive range of studies [20]. For instance, a SEM on a chemical might aim to identify all mammalian animal bioassays and epidemiological studies relevant to human hazard identification.

• Comprehensive Search and Screening: A systematic search is conducted across multiple scientific databases using a pre-defined search strategy. Standard systematic review practices, including the use of two independent reviewers per record and specialized software, are employed to screen titles, abstracts, and full texts against the eligibility criteria [4] [20]. Machine learning tools may be incorporated to facilitate this process.

• Data Extraction and Coding: Data from included studies is extracted using structured forms. This involves cataloging key study characteristics (e.g., design, test system, health outcomes assessed) and coding them using controlled vocabularies. This step is crucial for transforming unstructured literature into a structured, queryable format [66]. The availability of New Approach Methodologies (NAMs) is also frequently tracked [20].

• Database Creation and Visualization: The extracted data is stored in an interactive database. The data is then made accessible through interactive visualizations and can be downloaded in open-access formats, allowing end-users to explore the evidence base according to their specific interests [20].

The Scientist's Toolkit: Essential Reagents for SEM Implementation

Table 1: Key Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Specific Tool/Software	Primary Function in SEM Development
Literature Databases	PubMed, Scopus, Web of Science	Identification of primary research studies through systematic search strategies [20].
Systematic Review Software	DistillerSR, Rayyan, CADIMA	Facilitation of the literature screening process (title/abstract, full-text) by multiple independent reviewers [20].
Data Extraction Tools	Custom web-based forms, Excel templates	Standardized extraction of predefined data and metadata from included studies into a structured format [20].
Data Storage Solutions	SQL databases, Knowledge Graphs	Storage of extracted data in a flexible, queryable format. Knowledge graphs are particularly suited for complex, interconnected data [66].
Visualization Platforms	Tableau, R Shiny, EviAtlas	Creation of interactive dashboards and maps to explore the evidence base visually and identify trends [20].

Application of SEMs in REACH and TSCA Frameworks

Enhancing the EU REACH Process

The REACH regulation requires companies to register chemical substances manufactured or imported in quantities exceeding one tonne per year, involving the evaluation of submitted information and management of substances of very high concern (SVHCs) [91]. The 2025 revisions to REACH emphasize digitalization, enhanced enforcement, and simplified processes, creating a greater need for efficient evidence management [92] [93]. SEMs directly support REACH through:

• Priority Setting for Evaluation: Regulatory bodies like ECHA can use SEMs to rapidly assess the breadth and quality of evidence available for hundreds of chemicals. This helps identify substances with substantial, concerning toxicity data that warrant deeper evaluation or restriction, thereby optimizing the evaluation workload [4].
• Informing Grouped Assessment and PFAS Restrictions: The 2025 REACH revisions promote grouped assessment for chemical families, such as PFAS (Per- and polyfluoroalkyl substances) [93]. SEMs are ideal for mapping the entire evidence landscape for a chemical group, identifying common outcomes, and highlighting data-poor members, thereby providing a solid foundation for developing a unified assessment approach.
• Supporting Authorisation and Restriction: For SVHCs, SEMs can provide a transparent overview of the evidence justifying their classification and the availability of safer alternatives, which is central to the authorisation process [4] [91].

Strengthening US TSCA Risk Evaluations

Under TSCA, the EPA must conduct risk evaluations for existing chemicals to determine whether they present an unreasonable risk to health or the environment under the conditions of use [94] [26]. The process is resource-intensive and must adhere to strict statutory timelines. SEMs augment the TSCA workflow by:

• Scoping and Problem Formulation: In the initial scoping phase of a TSCA risk evaluation, a SEM can provide a high-level overview of the available hazard and exposure literature. This helps the EPA refine the scope, conceptual model, and analysis plan by identifying which health effects, exposure pathways, and populations are well-studied [26] [20].
• Ensuring Comprehensive Evidence Base: By employing a systematic and comprehensive search, SEMs help ensure that all relevant evidence is identified, reducing the risk of "cherry-picking" studies and increasing stakeholder confidence in the transparency of the assessment [4].
• Guiding Resource Allocation: With a mandate to continuously evaluate high-priority substances, the EPA can use SEMs as a triage tool to compare evidence bases across candidate chemicals, ensuring that the most critical substances with the most pressing data needs are advanced into full risk evaluations [4] [26].

Table 2: Quantitative Impact of SEMs on Regulatory Efficiency

Regulatory Challenge	Traditional Approach	SEM-Supported Approach	Impact of SEM
Problem Formulation	Relies on expert knowledge and limited literature reviews, potentially missing key studies.	Provides a comprehensive, transparent map of all evidence, revealing clusters and gaps.	Increases transparency, reduces bias, and provides an evidence-based foundation for scoping [4] [20].
Chemical Prioritization	May be influenced by high-profile studies or political pressure rather than systematic evidence.	Enables quantitative comparison of evidence volume and focus across multiple chemicals.	Improves resource allocation by targeting chemicals with the strongest evidence of hazard [4].
Data Gap Identification	Often discovered during the deep assessment phase, causing delays.	Highlighted at the outset via the evidence map.	Allows for proactive planning of testing strategies or assessment approaches, saving time [4] [64].
Stakeholder Communication	Difficult to demonstrate the full body of considered evidence.	Interactive maps and visualizations provide a clear, accessible summary of the evidence landscape.	Builds trust and facilitates engagement by making the evidence base explorable [20].

Integrated Workflow: SEMs in Chemical Regulation

The following diagram illustrates how SEMs are integrated into the broader chemical risk management workflows of REACH and TSCA, acting as a bridge between vast scientific literature and decisive regulatory action.

Systematic Evidence Maps represent a paradigm shift in how regulatory bodies can manage the deluge of scientific data in chemical risk assessment. By providing a structured, transparent, and queryable overview of broad evidence bases, SEMs directly support the core objectives of REACH and TSCA: to protect human health and the environment efficiently and based on the best available science. They enable resource-efficient priority setting, inform problem formulation, and ensure that subsequent, more resource-intensive assessments like systematic reviews are deployed where they are most needed. As chemical regulations continue to evolve, the adoption of SEMs will be crucial for building responsive, transparent, and evidence-based regulatory frameworks.

Conclusion

Systematic Evidence Maps represent a paradigm shift in chemical assessment, offering a robust, transparent, and resource-efficient methodology for navigating complex evidence landscapes. By providing comprehensive overviews of available research while precisely identifying critical knowledge gaps—as starkly revealed in assessments showing that over 98% of PFAS chemicals lack health effects data—SEMs enable strategic prioritization in both research and regulatory decision-making. The integration of advanced technologies like knowledge graphs and machine learning promises to further enhance their scalability and utility. For biomedical and clinical research, the continued evolution and standardization of SEM methods will be crucial for addressing the growing challenges of chemical risk assessment, ultimately leading to more informed, evidence-based public health protections and more efficient drug development pipelines. Future directions should focus on developing standardized reporting guidelines, expanding cross-agency collaboration, and further integrating New Approach Methodologies (NAMs) into evidence mapping frameworks.

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