The FDA/CDER Chemical Informatics Program

Transcription

1 The FDA/CDER Chemical Informatics Program Naomi Kruhlak, PhD Lead, Chemical Informatics Program Division of Applied Regulatory Science Office of Clinical Pharmacology Office of Translational Sciences FDA Center for Drug Evaluation and Research Society of Toxicology Annual Meeting, Joint Vendor Symposium March 23,

2 Disclaimer The findings and conclusions in this presentation have not been formally disseminated by the FDA and should not be construed to represent any agency determination or policy. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services. 2

3 FDA/CDER Chemical Informatics Program An applied regulatory research group that: Creates chemical structure-linked toxicological and clinical effect databases Develops rules for quantifying in vitro, animal and human endpoint data Evaluates data-mining and (Q)SAR software Develops toxicological and clinical effect prediction models through collaborations with software companies The Computational Toxicology Consultation Service that: Provides (Q)SAR evaluations for drugs, metabolites, contaminants, degradants, etc. to FDA/CDER safety reviewers Performs structure-similarity searching for read-across purposes Provides expert interpretation of (Q)SAR data submitted to FDA/CDER 3

4 (Q)SAR Modeling: What is it? Identifies correlations between chemical structural features and biological activity Uses the results of actual laboratory testing or clinical outcomes General assumption: Similar molecules exhibit similar physicochemical and biological properties Make prediction of a compound s biological activity based on its chemical structure rapidly consistently QSAR quantitative statistically-derived model SAR qualitative expert rule-based model (Q)SAR 4

5 Building a (Q)SAR Model Chemical Structures (Descriptors) Known Activity Data (Q)SAR Algorithm (Q)SAR Model Activity Prediction 5

6 Regulatory Research Activities Endpoints of regulatory interest Non-Clinical Genetic Toxicity Rodent Carcinogenicity Reproductive and Developmental Tox Phospholipidosis Current Activities Clinical Drug-Induced Liver Injury Cardiovascular Toxicity Renal Toxicity Database expansion and curation Improving models to fill data gaps and provide better prediction resolution in some areas of chemical space Developing web-based tools for reviewers to directly query our in-house knowledge bases Benchmarking of (Q)SAR tools used by sponsors 6

7 FDA / CDER Computational Toxicology Consultation Service Majority of requests received for ICH M7 compliant genotoxicity (Q)SAR assessments Synthetic intermediates, starting materials, reagents, degradants (Q)SAR may be used to qualify an impurity for genotoxic potential in place of an in vitro assay Capabilities offered (Q)SAR analysis using multiple software platforms Structure similarity searching of public, commercial and in-house databases Expert interpretation of (Q)SAR data submitted to FDA/CDER Online submission form FDA Intranet Automated follow-up questions requesting feedback 7

8 Concluding Remarks More chemical informatics-based data are being submitted to CDER by Sponsors (Q)SAR assessments Structural analog data Role of FDA/CDER Computational Toxicology Consultation Service evolving under ICH M7: Running entire analysis Filling prediction and/or data gaps Interpreting complete Sponsor submissions Underscores the need for adequate reporting of (Q)SAR assessments included in regulatory submissions 8

9 Acknowledgements Chemical Informatics Team Members: Barbara Minnier Dongyu Guo Neil Hartman Andy Fant Support: Critical Path Initiative MCM Initiative Mark Powley Jae Wook Yoo Kurt Hewes ORISE RCA partners Contact Information: and 9