Development of Institutional Knowledge-base at FDA for Food Safety

1. Development of an Institutional Knowledge-base at FDA’s Center for Food Safety and Applied Nutrition Kirk B. Arvidson1, Annette McCarthy1, Chihae Yang2, Dimitar Hristozov1 1)U.S. Food and Drug Administration (FDA) Center for Food Safety and Applied Nutrition (CFSAN) Office of Food Additive Safety (OFAS) 2) The Ohio State University, Columbus, Ohio

2. Outline Introduction CERES workflow Database scheme Query Results QSAR models Q&A

3. Office Of Food Additive Safety (OFAS) OFAS is a program office within CFSAN Ensure the safety of food additives and packaging in U.S. Evaluate safety information in industry submissions for various categories of food ingredients Direct food additives (e.g., high intensity sweeteners) Biotech foods (e.g., herbicide-ready soybeans) Generally Recognized as Safe (GRAS – phosphoric acid) Food contact substances (e.g., plastic bottles, sanitizers)

4. Pre- and Post-market Evaluations • Shortfall of current processes • Rely on institutional knowledge • Data is sequestered in multiple small databases • Accessing/locating data can be difficult and time consuming • Difficult to keep data up to date-time and resource intensive • No systematic method to bring together disparate tox. data on a compound or its structural and biological analogues • Current needs • A centralized knowledgebase accessible from desktop computer • Real time access to data-both internal and external sources • Relate new and existing data in new ways • Leveraging knowledge more efficiently • Make sound decisions faster

5. Chemical Evaluation and Risk Estimation System (CERES) • Food additives knowledge-base • Captures institutional knowledge • Chemical centric • Structured data/controlled vocabulary • Desktop access to: • Internal and external chemical and toxicity data • Structure analog searching and data retrieval • QSAR Models • Threshold of Toxicological Concern evaluations

6. Decision Support for Safety Assessment Decision Support for Safety Assessment Decision Support for Safety Assessment Decision Support for Safety Assessment Decision Support for Safety Assessment Decision Support for Safety Assessment Pre-market Safety Decisions Pre-market Safety Decisions Pre-market Safety Decisions Pre-market Safety Decisions Pre-market Safety Decisions Pre-market Safety Decisions • Computational Toxicology • Read-across • Rule-base • Mode of action models • Threshold of toxicological • concern (TTC) • Computational Toxicology • Read-across • Rule-base • Mode of action models • Threshold of toxicological • concern (TTC) • Computational Toxicology • Read-across • Rule-base • Mode of action models • Threshold of toxicological • concern (TTC) • Computational Toxicology • Read-across • Rule-base • Mode of action models • Threshold of toxicological • concern (TTC) • Computational Toxicology • Read-across • Rule-base • Mode of action models • Threshold of toxicological • concern (TTC) • Computational Toxicology • Read-across • Rule-base • Mode of action models • Threshold of toxicological • concern (TTC) Knowledgebase Knowledgebase Knowledgebase Knowledgebase Knowledgebase Knowledgebase Pre-market Review Portal Pre-market Review Portal Pre-market Review Portal Pre-market Review Portal Pre-market Review Portal Pre-market Review Portal Submission Data Literature Data Submission Data Literature Data Submission Data Literature Data Submission Data Literature Data Submission Data Literature Data Submission Data Literature Data Post-market Review Portal Post-market Review Portal Post-market Review Portal Post-market Review Portal Post-market Review Portal Post-market Review Portal Results Set Results Set Results Set Results Set Results Set Results Set Post-market Safety Decisions Post-market Safety Decisions Post-market Safety Decisions Post-market Safety Decisions Post-market Safety Decisions Post-market Safety Decisions Safety Alert Safety Alert Safety Alert Safety Alert Safety Alert Safety Alert CERES Workflow

7. Simplified Database Scheme

8. Query Page

9. Query Results: Regulatory Summary

10. Query Results: Study Summary

11. Prediction Models

12. Categories Alerts MoA classes .... Global Aldehydes Aromatic Amines Alkyl halides Phenols N H 2 0.621 0.588 0.886 H O Weight of evidence averaging 0.723 Likely mutagenic Not likely mutagenic Weight of evidence and mode of action models

13. Repro-Green Dev.-Blue Structure Categories Carc.-Red ????-Grey Database TTC Values TTC values Enhance Pre-, Post-market Evaluation of Food Ingredients Through Threshold of Toxicological Concern Approach

14. Repro-Green Dev.-Blue Carc.-Red Structure Categories ????-Grey Enhance Pre-, Post-market Evaluation of Food Ingredients Query Exposure TTC values Regulatory Action Additional Data Needs

15. Profiling Toxicological Concerns With Chemical and Biological Classes concern factor Biological endpoints Chemical Classes Red: high concern Yellow: intermediate Green: low concern

16. Computational Toxicology Paradigm

17. toxicity bioassays ToxCastTM and Tox21 • Molecular signatures – biology/chemistry • High Throughput Screening assays (>500) • cell-based assays • cell-free target-based assays • Leverage legacy toxicity data Navigation through chemical classes Red: high corr.; Blue: low corr.

18. Conclusion • CERES • Improve Pre- and Post-Market Review • Consolidates information on chemical structure, physical properties and toxicity data to allow for more robust safety analysis • Semi-automated monitoring of new safety data relative to authorized chemicals through the use of computational toxicology and TTC • Relate new and existing data in new ways • Seek biologically meaningful analogs to fill the data gaps found in many food ingredients and food contact substances • Provide molecular level mechanistic insights that eventually help us understand human effects • Metabolism knowledge will also be incorporated.