lecture at UNC in November 2011

1. Sean Ekins, M.Sc, Ph.D., D.Sc. Collaborations in Chemistry, Fuquay-Varina, NC. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland. 215-687-1320 [email_address] Computational Models for Predicting Human Toxicities

3. Why Use Computational Models For Toxicology ? Goal of a model – Alert you to potential toxicity, enable you to focus efforts on best molecules – reduce risk Selection of model – trade off between interpretability, insights for modifying molecules, speed of calculation and coverage of chemistry space – applicability domain Models can be built with proprietary, open and commercial tools software (descriptors + algorithms) + data = model/s Human operator decides whether a model is acceptable

4. Key enablers: Hardware is getting smaller 1930’s 1980s 1990s Room size Desktop size Not to scale and not equivalent computing power – illustrates mobility Laptop Netbook Phone Watch

9. L. Carlsson,et al., BMC Bioinformatics 2010, 11: 362 MetaPrint 2D in Bioclipse- free metabolism site predictor Uses fingerprint descriptors and metabolite database to learn frequencies of metabolites in various substructures

13. hOCTN2 – Organic Cation transporter Pharmacophore Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

14. Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009) +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine

15. hOCTN2 quantitative pharmacophore and Bayesian model Bayesian Model - Leaving 50% out 97 times external ROC 0.90 internal ROC 0.79 concordance 73.4%; specificity 88.2%; sensitivity 64.2%. Lab test set (N = 27) Bayesian model has better correct predictions (> 80%) and lower false positives and negatives than pharmacophore (> 70%) Predictions for literature test set (N=32) not as good as in house – mean max Tanimoto similarity were ~ 0.6 Diao et al., Mol Pharm, 7: 2120-2131, 2010 PCA used to assess training and test set overlap

16. Among the 21 drugs associated with rhabdomyolysis or carnitine deficiency, 14 (66.7%) provided a C max/ K i ratio higher than 0.0025. Among 25 drugs that were not associated with rhabdomyolysis or carnitine deficiency, only 9 (36.0%) showed a C max / K i ratio higher than 0.0025. Rhabdomyolysis or carnitine deficiency was associated with a C max / K i value above 0.0025 (Pearson’s chi-square test p = 0.0382). limitations of C max / K i serving as a predictor for rhabdomyolysis -- C max / K i does not consider the effects of drug tissue distribution or plasma protein binding. hOCTN2 association with rhabdomyolysis

22. Fingolimod (Gilenya) for MS (EMEA and FDA) Paliperidone for schizophrenia Pirfenidone for Idiopathic pulmonary fibrosis Roflumilast for pulmonary disease Predictions for newly approved EMEA compounds Can we get DILI data for these?

25. http://www.slideshare.net/ekinssean Ekins S and Williams AJ, MedChemComm, 1: 325-330, 2010. Analysis of malaria and TB datasets

26. Antimalarial Compound libraries and filter failures Ekins and Williams Drug Disc Today 15; 812-815, 2010 Filtering using SMARTs filters to remove thiol reactives, false positives etc at University of New Mexico (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter) % Failure

27. TB Compound libraries and filter failures Filtering using SMARTs filters to remove thiol reactives, false positives etc at University of New Mexico (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter) Ekins et al., Mol Biosyst, 6: 2316-2324, 2010

28. Correlation between the number of SMARTS filter failures and the number of Lipinski violations for different types of rules sets with FDA drug set from CDD (N = 2804) Suggests # of Lipinski violations may also be an indicator of undesirable chemical features that result in reactivity Correlations Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011.

29. Could all pharmas share their data as models with each other? Increasing Data & Model Access Ekins and Williams, Lab On A Chip, 10: 13-22, 2010.

34. Merck KGaA Combining models may give greater coverage of ADME/ Tox chemistry space and improve predictions? Model coverage of chemistry space Lundbeck Pfizer Merck GSK Novartis Lilly BMS Allergan Bayer AZ Roche BI Merk KGaA

38. Government Databases Should Come With a Health Warning Openness Can Bring Serious Quality Issues NPC Browser http://tripod.nih.gov/npc/ Database released and within days 100’s of errors found in structures Williams and Ekins, DDT, 16: 747-750 (2011) Science Translational Medicine 2011