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Development of an Institutional Knowledge-base at FDA’s Center for Food Safety and Applied Nutrition Kirk B. Arvidson 1, Annette McCarthy 1, Chihae Yang.

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Presentation on theme: "Development of an Institutional Knowledge-base at FDA’s Center for Food Safety and Applied Nutrition Kirk B. Arvidson 1, Annette McCarthy 1, Chihae Yang."— Presentation transcript:

1 Development of an Institutional Knowledge-base at FDA’s Center for Food Safety and Applied Nutrition Kirk B. Arvidson 1, Annette McCarthy 1, Chihae Yang 2, Dimitar Hristozov 1 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 CERES Workflow Decision Support for Safety Assessment Pre-market Safety Decisions Post-market Safety Decisions Post-market Review Portal Pre-market Review Portal Submission Data Literature Data Computational Toxicology Read-across Rule-base Mode of action models Threshold of toxicological concern (TTC) Safety Alert Knowledgebase Results Set Decision Support for Safety Assessment Pre-market Safety Decisions Post-market Safety Decisions Post-market Review Portal Pre-market Review Portal Submission Data Literature Data Computational Toxicology Read-across Rule-base Mode of action models Threshold of toxicological concern (TTC) Safety Alert Knowledgebase Results Set Decision Support for Safety Assessment Pre-market Safety Decisions Post-market Safety Decisions Post-market Review Portal Pre-market Review Portal Submission Data Literature Data Computational Toxicology Read-across Rule-base Mode of action models Threshold of toxicological concern (TTC) Safety Alert Knowledgebase Results Set Decision Support for Safety Assessment Pre-market Safety Decisions Post-market Safety Decisions Post-market Review Portal Pre-market Review Portal Submission Data Literature Data Computational Toxicology Read-across Rule-base Mode of action models Threshold of toxicological concern (TTC) Safety Alert Knowledgebase Results Set Decision Support for Safety Assessment Pre-market Safety Decisions Post-market Safety Decisions Post-market Review Portal Pre-market Review Portal Submission Data Literature Data Computational Toxicology Read-across Rule-base Mode of action models Threshold of toxicological concern (TTC) Safety Alert Knowledgebase Results Set Decision Support for Safety Assessment Pre-market Safety Decisions Post-market Safety Decisions Post-market Review Portal Pre-market Review Portal Submission Data Literature Data Computational Toxicology Read-across Rule-base Mode of action models Threshold of toxicological concern (TTC) Safety Alert Knowledgebase Results Set

7 Simplified Database Scheme

8 Query Page

9 Query Results: Regulatory Summary

10 Query Results: Study Summary

11 Prediction Models

12 Weight of evidence and mode of action models MoA classes Aldehydes PhenolsAromatic AminesAlkyl halides Global Not likely mutagenic Categories Likely mutagenic.... Alerts NH 2 OH Weight of evidence averaging 0.588

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

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

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

16 Computational Toxicology Paradigm

17 ToxCast TM and Tox21 Molecular signatures – biology/chemistry High Throughput Screening assays (>500) – cell-based assays – cell-free target-based assays Leverage legacy toxicity data toxicity bioassays 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.


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