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Development of a Tool for Systematic Integration of Traditional and New Approach Methods for Prioritizing Chemical Lists

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Multiple regulatory bodies (EPA, ECHA, Health Canada) are currently tasked with prioritizing chemicals for data collection and risk assessments. These prioritization efforts are in response to regulatory mandates to identify chemicals for further assessment. We have developed a web-based application that enables a rapid, flexible and transparent prioritization process. The tool includes multiple data streams related to human and ecological hazard, exposure, and physicochemical properties (persistence and bioaccumulation). For human hazard, the data streams include quantitative points of departure (PODs) that are compiled from multiple sources such as EPA ToxRefDB, ECHA, COSMOS; estimated PODs from high-throughput in vitro screening assays and computational models; and qualitative measurements and predictions of specific endpoints (e.g., genotoxicity, endocrine activity). For ecological hazard, quantitative PODs are taken from the EPA ECOTOX database. Exposure information includes production volume, quantitative predictions using the EPA ExpoCast and SHEDS models, biomonitoring data, and qualitative information such as media occurrence, use profiles and likelihood of consumer and childhood exposures. The use of the tool is illustrated by prioritizing chemicals related to TSCA and the Safer Choice Ingredient List. The underpinning data streams for this application are already available in the EPA CompTox Chemistry Dashboard and have been repurposed to deliver this application. This is in keeping with our overarching software development methodology of providing multiple “building blocks” in the form of databases, web services and visualization components to deliver fit-for purpose applications to the relevant audiences. This abstract does not necessarily represent U.S. EPA policy.

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Development of a Tool for Systematic Integration of Traditional and New Approach Methods for Prioritizing Chemical Lists

  1. 1. Development of a Tool for Integrating Traditional and New Approach Methodologies (NAMs) for Chemical Safety Decisions Antony Williams, Richard Judson, Chris Grulke and Rusty Thomas National Center for Computational Toxicology, U.S. Environmental Protection Agency, RTP, NC March 2018 ACS Spring Meeting, New Orleans http://www.orcid.org/0000-0002-2668-4821 The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA
  2. 2. • National Center for Computational Toxicology (NCCT) established in 2005 to integrate: – High-throughput and high-content technologies – Molecular biology – Data mining and statistical modeling – Computational biology and chemistry • Outputs: a lot of data, models, algorithms and software applications • Open Data – we want scientists to interrogate it, learn from it, develop understanding National Center for Computational Toxicology
  3. 3. Background • EPA must designate a set of high-priority chemicals for detailed risk assessment. • NCCT is building a web-based tool as one approach to help guide that selection 2
  4. 4. The CompTox Chemistry Dashboard • NCCT has lots of data to build relevant tools and approaches. The source data are available via the CompTox Chemistry Dashboard • A publicly accessible website delivering access: – ~760,000 chemicals with related property data – Experimental and predicted physicochemical property data – Integration to “biological assay data” for 1000s of chemicals – Data regarding chemical exposure – Links to other agency websites and public data resources – “Literature” searches for chemicals using public resources – “Batch searching” for thousands of chemicals 3
  5. 5. CompTox Chemistry Dashboard https://comptox.epa.gov/dashboard 4
  6. 6. Chemical Hazard Data 5
  7. 7. In Vitro Bioassay Screening ToxCast and Tox21 6
  8. 8. Sources of Exposure to Chemicals 7
  9. 9. Approach • Use available data for in vivo, in vitro, exposure and chemical property data • Develop scoring schemes to merge different types of data • Develop methods to note or fill data gaps • Make data, scores, prioritization ranking available in the web-based tool 8
  10. 10. Potential Data Sources In Vivo Human Hazard: • Mammalian toxicity studies – guideline-like, use POD • System-specific in vivo data (Cancer, developmental) • Models (QSAR) to predict POD and organ-specific effects • Genotoxicity • In vitro-derived endocrine disruption and neurotoxicity models In Vivo Eco Hazard • Aquatic in vivo studies – POD • Models (QSAR) of POD 9
  11. 11. Human Exposure • Data on production volume and releases • Quantitative biomonitoring data • Predictions of oral and inhalation exposure Eco Exposure • Biomonitoring data • Predictions of water concentrations Physchem • Persistence and Bioaccumulation models (OPERA) 10 Potential Data Sources
  12. 12. Aggregating Data 11 DOMAINS: Top level aggregation for reporting in the web-interface SUB-DOMAINS: Additional aggregation layer(s) to support flexible aggregation workflows ENDPOINTS: Aggregation of data streams that are intended to estimate the same value DATA STREAMS: Individual values coming from a single source that are of interest in prioritization. DATABASES: Active database projects at US EPA can act as sources for data streams Human Hazard Persistence Acute Chronic Sub-Chronic Endocrine Biotrans. Bioaccum. BAF BCF Fish Km Biodeg. Acute POD ER Binding In Vivo Rat LD50 T.E.S.T Rat LD50 Tox21 ERa Antagonist CERAPP Predictions In Vivo BCF OPERA BCF ChemProp ToxVal ToxCast ECOTOX
  13. 13. Scoring approaches • For each chemical, each domain receives a score of 1 (Low), 2 (Moderate), or 3 (High) concern • Hazard score = maximum of human and eco hazard scores • Exposure score = maximum of human and eco exposure scores • Total score = hazard score + exposure score + physchem score • If no data is available for a domain, it is given the “missing data score”, currently 1 (Low) • Scoring can include or exclude NAM 12
  14. 14. Implemented Scoring Methods 13 Method 1 Method 2: NAM Equal Method 3: NAM Deferential • Maximum score from human and eco hazard: 1 – 3 • Maximum score from human and eco exposure: 1 – 3 • Maximum score from persistence/ bioaccumulation (P/B): 1 – 3 • No NAM • Add hazard, exposure, and P/B • Categorical bins • High: 7-9 • Moderate: 5-6 • Low: 3-4 • Same as Method 1 except NAM is incorporated with equal weighting in all domains • Add hazard, exposure, and P/B • Categorical bins • High: 7-9 • Moderate: 5-6 • Low: 3-4 • Same as Method 1 except human hazard NAM is incorporated in the absence of traditional in vivo studies • In other domains, NAM is given equal weight. • Add hazard, exposure, and P/B • Categorical bins • High: 7-9 • Moderate: 5-6 • Low: 3-4 Method 4: Sum of Scores Method 5: H/BER* • Sum all components (incl. NAM) from human and eco hazard • Sum all components (incl. NAM) from human and eco exposure • Sum all components (incl. NAM) from persistence/ bioaccumulation • Add hazard, exposure, and P/B • Categorical bins • High: >30 • Medium: 10-30 • Low: ≤10 • Ratio of the minimum effect level from in vivo toxicity studies or the quantitative human hazard NAM data divided by the maximum oral exposure • Categorical bins • High: ≤104 • Medium: 104 – 106 • Low: ≥106 *Hazard/Bioactivity Exposure Ratio
  15. 15. Overall Scoring Page 14
  16. 16. Fraction of chemicals in each bin 15 High Pre-Priority Moderate Pre-Priority Low Pre-Priority General NAM Equal NAM Differential Sum of Scores H/BER Set 1 (344) Set 2 (867) (233/867) (222/344)
  17. 17. Heat Maps 16 Chemicals Human Hazard Eco Hazard Human Exposure Eco Exposure Persistence /Bioaccum Human Hazard Eco Hazard Human Exposure Eco Exposure Persistence /Bioaccum Heatmaps showing the domain-specific scores for chemical sets. High Score (3) Moderate Score (2) Low Score (1) No Data
  18. 18. Registration and Curation of Chemicals • Consolidation and registration of the original chemical lists into the underlying database (DSSTox) • Careful (and time-consuming) curation – Confirming mappings of chemical names and CASRNs 17
  19. 19. Names to CASRN Mappings 18
  20. 20. Subtleties 19 E/Z-stereochemistry E-stereochemistry “4-Decene”
  21. 21. Registration and Curation • Registration of the chemical list into the underlying database (DSSTox) • Careful (and time-consuming) curation – Confirming mappings of chemical names and CASRNs – CASRN checking – Active, Alternate and Deleted 20
  22. 22. CAS Registry Numbers 21
  23. 23. Registration and Curation • Registration of the chemical list into the underlying database (DSSTox) • Careful (and time-consuming) curation – Confirming mappings of chemical names and CASRNs – CASRN checking – Active, Alternate and Deleted – Misspellings, alternative synonyms, misassociations 22
  24. 24. Alternative Synonyms 23
  25. 25. UVCB Chemicals • UVCBs are chemical substances of unknown or variable composition, complex reaction products and biological materials – Surfactants (C11-14 linear alkyl sulfonates) – Reaction mass of p-t-butylphenyldiphenyl phosphate and bis(p-t-butylphenyl)phenyl phosphate and triphenyl phosphate – Almond Oil 24
  26. 26. Di-sec-butylphenol 25 CAS Representation Dashboard Representation
  27. 27. “Markush Structures” https://en.wikipedia.org/wiki/Markush_structure 26
  28. 28. Summary • RapidTox workflow enabled identification of data that contributed most to candidate selection • Allowed flexible exploration of prioritization methods • Incorporation of NAM data does result in changes for chemical test sets, either by adding data or by changing the overall bin (Low, Moderate, High) 27
  29. 29. Acknowledgments • The NCCT CompTox Chemistry Dashboard Development Team • Kamel Mansouri – OPERA models • Todd Martin – TEST predictions 28
  30. 30. Contact Antony Williams US EPA Office of Research and Development National Center for Computational Toxicology (NCCT) Williams.Antony@epa.gov ORCID: https://orcid.org/0000-0002-2668-4821 29

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