Talk for BioIT World 2011 based on published work Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

1. A Predictive Ligand-Based Bayesian Model for Human Drug Induced Liver Injury Sean Ekins Collaborations in Chemistry, Jenkintown, PA. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland.

2. Pharma reached a productivity tipping point Cost of drug development high Failure in clinic due to toxicity How to predict failure earlier

4. The future: crowdsourced drug discovery Williams et al., Drug Discovery World, Winter 2009

6. Drug Examples for DILI + and - Troglitazone DILI + Pioglitazone DILI - Rosiglitazone DILI - Sulindac DILI + Aspirin DILI - Diclofenac DILI + Xu et al., Toxicol Sci 105: 97-105 (2008).

13. Bayesian machine learning Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Bayesian classification is a simple probabilistic classification model. It is based on Bayes’ theorem h is the hypothesis or model d is the observed data p ( h ) is the prior belief (probability of hypothesis h before observing any data) p ( d ) is the data evidence (marginal probability of the data) p ( d|h ) is the likelihood (probability of data d if hypothesis h is true) p ( h|d ) is the posterior probability (probability of hypothesis h being true given the observed data d ) A weight is calculated for each feature using a Laplacian-adjusted probability estimate to account for the different sampling frequencies of different features. The weights are summed to provide a probability estimate

14. Examples of using Bayesian Models Integrated in Silico-in Vitro Strategy for Addressing Cytochrome P450 3A4 Time-Dependent Inhibition Zientek et al., Chem Res Toxicol 23: 664-676 (2010) Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR Ekins S, Kortagere S, Iyer M, Reschly EJ, Lill MA, Redinbo MR and Krasowski MD, PLoS Comput Biol 5(12): e1000594, (2009) . Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter Zheng X, et al., Mol Pharm, 6: 1591-1603, (2009) Quantitative structure activity relationship for inhibition of human organic cation/carnitine transporter Diao et al., Mol Pharm, 7: 2120-2131, (2010)

15. Features in DILI + Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Avoid Long aliphatic chains Phenols Ketones Diols  -methyl styrene Conjugated structures Cyclohexenones Amides ?

16. Features in DILI - Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

19. Training vs test set PCA Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Yellow = test Blue = training retinyl palmitate

21. 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 experimental data for these?

22. SMARTS FIlters Smartsfilter kindly provided by Dr. Jeremy Yang (University of New Mexico, Albuquerque, NM, http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter). Substructure Alerts used to filter libraries – remove reactive groups etc. Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

23. SMARTS Filters vs Rule of 5 Ekins and Freundlich, Pharm Res, In press 2011 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

26. Pfizer Merck GSK Novartis Lilly BMS Could combining models give greater coverage of ADME/ Tox chemistry space and improve predictions? Lundbeck Allergan Bayer AZ Roche BI Merk KGaA Expanding computational model coverage of chemical space

27. More collaborations, integrating models into scientific social networks Drug Disc Today, 14: 261-270, 2009