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ToxGPS Models V. Gombar 1 , J.F. Rathman 1,3 , C.H. Schwab 2 , A. Mostrag 1 , B. Bienfait 2 , T. Magdziarz 2 , O. Sacher 2 , C. Yang 1,2 * A Reliable Workflow for In Silico Assessment of Genetic Toxicity and Application to Pharmaceutical Genotoxic Impurities *[email protected] 1 Altamira LLC, Columbus OH, USA; 2 Molecular Networks GmbH, Erlangen, Germany; 3 Chemical and Biomolecular Engineering. The Ohio State University, Columbus, OH, USA Search Database o ChemTunes/ToxGPS o A comprehensive knowledgebase - experimental toxicity data and predictive models o QSAR modeling based on biologically meaningful grouping using mechanistically selected chemotypes and molecular descriptors o Final outcome combines the evidences of QSAR models and chemotype rule-based predictions to provide good prediction performance o Robust risk assessment system providing rigorous method for quantitative weight-of- evidence o In two open challenges involving over 8,000 compounds, ToxGPS Ames mutagenicity model ranked highly for GTI relevant statistics. References 1 CORINA Symphony, Molecular Networks GmbH, Erlangen, Germany 2 Yang, C. et al. (2015) J Chem Inf Model 55(3), 510-528. Summary Background o Drug products generally contain more than just the active pharmaceutical ingredient (API) o Collectively called "impurities" o Impurities during synthesis, storage, etc., no medical benefit o Genotoxic impurities - induce genetic mutations, chromosomal breaks, and/or chromosomal rearrangements o For patient safety, identification and control of impurities needed ICH M7 Compliance – In Silico Tools ICH M7 Guidance Find Data or “predict toxicity” ToxGPS GTI Workflow MoA models, Chemotype alerts, Nearest neighbors CORINA Symphony 1 Properties o Global molecular descriptors o Shape and size descriptors o Semi-empirical molecular orbital parameters ToxPrint chemotypes o Public library of chemotypes 2 o Toxicity-related features relevant to human & environment safety o Generic compound classes Bacterial reverse mutagenesis (Ames mutagenicity) model information chemtunes.com toxprint.org chemotyper.org o Ames Mutagenicity prediction challenge by NIHS Japan Phase 1: Test set with 3,950 compounds 16 participants Phase 2: Test set with 3,840 compounds 18 participants Phase 1 results were provided to participants and could be incorporated into models developed for Phase 2 o ToxGPS Ames Model Excellent performance in both phases ToxGPS Ames Model Performance o Assay Load: If an impurity is not predicted to be negative, then it must be tested experimentally. False positives unnecessarily increase the assay load. o Risk: Impurities that are genotoxic but predicted to be negative present a product risk. o The ToxGPS Ames model performs well with respect to these two important metrics: Load Rank (e.g., ranked 4th out of 18 in phase 2) Risk Rank (e.g., ranked 3rd out of 16 in phase 1 in false negatives rate) Modeling approaches Model descriptions – selection of predictors Overall prediction based on weight-of-evidence
Transcript of A Reliable Workflow for In Silico Assessment of Genetic ... · A Reliable Workflow for In Silico...
ToxGPS Models
V. Gombar1, J.F. Rathman1,3, C.H. Schwab2, A. Mostrag1, B. Bienfait2, T. Magdziarz2,
O. Sacher2, C. Yang1,2*
A Reliable Workflow for In Silico Assessment of Genetic Toxicity
and Application to Pharmaceutical Genotoxic Impurities
*[email protected] LLC, Columbus OH, USA; 2Molecular Networks GmbH, Erlangen, Germany; 3Chemical and
Biomolecular Engineering. The Ohio State University, Columbus, OH, USA
Search Database
o ChemTunes/ToxGPS
o A comprehensive knowledgebase - experimental toxicity data and predictive models
o QSAR modeling based on biologically meaningful grouping using mechanistically selected
chemotypes and molecular descriptors
o Final outcome combines the evidences of QSAR models and chemotype rule-based
predictions to provide good prediction performance
o Robust risk assessment system providing rigorous method for quantitative weight-of-
evidence
o In two open challenges involving over 8,000 compounds, ToxGPS Ames mutagenicity model
ranked highly for GTI relevant statistics.
References
1CORINA Symphony, Molecular Networks GmbH, Erlangen, Germany
2Yang, C. et al. (2015) J Chem Inf Model 55(3), 510-528.
Summary
Background
o Drug products generally contain more than just the active pharmaceutical ingredient (API)
o Collectively called "impurities"
o Impurities during synthesis, storage, etc., no medical benefit
o Genotoxic impurities - induce genetic mutations, chromosomal breaks, and/or chromosomal rearrangements
o For patient safety, identification and control of impurities needed
ICH M7 Compliance – In Silico Tools
ICH M7 Guidance
Find Data or “predict toxicity”
ToxGPS GTI Workflow
MoA models, Chemotype alerts, Nearest neighbors
CORINA Symphony1
Properties
o Global molecular
descriptors
o Shape and size
descriptors
o Semi-empirical
molecular orbital
parameters
ToxPrint chemotypes
o Public library of
chemotypes2
o Toxicity-related
features relevant to
human & environment
safety
o Generic compound
classes
Bacterial reverse mutagenesis
(Ames mutagenicity)
model information
chemtunes.com
toxprint.org
chemotyper.org
o Ames Mutagenicity prediction challenge by NIHS Japan
Phase 1: Test set with 3,950 compounds 16 participants
Phase 2: Test set with 3,840 compounds 18 participants
Phase 1 results were provided to participants and could be incorporated
into models developed for Phase 2
o ToxGPS Ames Model
Excellent performance in both phases
ToxGPS Ames Model Performance
o Assay Load: If an impurity is not predicted to be negative, then it must be tested
experimentally. False positives unnecessarily increase the assay load.
o Risk: Impurities that are genotoxic but predicted to be negative present a product risk.
o The ToxGPS Ames model performs well with respect to these two important metrics:
Load Rank (e.g., ranked 4th out of 18 in phase 2)
Risk Rank (e.g., ranked 3rd out of 16 in phase 1 in false negatives rate)
Modeling approachesModel descriptions – selection of predictors
Overall prediction based on weight-of-evidence
