Slides presented 19 th Sept 2011 in Helsinki

1. Application and Future of ADME/Tox Models Sean Ekins Collaborations in Chemistry, Fuquay-Varina, NC. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland.

2. Ekins et al., Trends Pharm Sci 26: 202-209 (2005) Consider Absorption, Distribution, Metabolism, Excretion and Toxicology properties earlier in Drug Discovery Combine in silico, in vitro and in vivo data - Approach equally applicable to consumer products and getting information on chemicals – REACH regulations etc.

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

5. 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 2000s

6. Models and software becoming more accessible- free, precompetitive efforts - collaboration

7. L. Carlsson,et al., BMC Bioinformatics 2010, 11: 362 MetaPrint 2D in Bioclipse- free metabolism site predictor

10. Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011. Correlation between the number of SMARTS filter (reactive features) 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 Simple Rules vs SMARTS filters

12. 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.

15. RRCK Permeability and MDR Open descriptors results almost identical to commercial descriptors Across many datasets and quantitative and qualitative data Smaller solubility datasets give similar results Provides confidence that open models could be viable MDCK training 25,000 testing 25,000 MDR training 25,000 testing 18,400 Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010 Kappa = 0.50 Sensitivity = 0.62 Specificity = 0.94 PPV = 0.68 Kappa = 0.53 Sensitivity = 0.64 Specificity = 0.94 PPV = 0.72 (Baseline) Kappa = 0.47 Sensitivity = 0.59 Specificity = 0.93 PPV = 0.67 C5.0 RRCK Permeability Kappa = 0.65 Sensitivity = 0.86 Specificity = 0.78 PPV = 0.84 CDK and SMARTS Keys Kappa = 0.67 Sensitivity = 0.86 Specificity = 0.80 PPV = 0.85 (Baseline) MOE2D and SMARTS Keys Kappa = 0.62 Sensitivity = 0.85 Specificity = 0.77 PPV = 0.83 CDK descriptors C5.0 MDR

16. 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

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

23. Examples of using Bayesian Models PXR Ekins S, et al.,, PLoS Comput Biol 5(12): e1000594, (2009) Pan Y et al Drug Metab Dispos, 39:337-344, (2011). human apical sodium-dependent bile acid transporter Zheng X, et al., Mol Pharm, 6: 1591-1603, (2009) Cytochrome P450 3A4 Time-Dependent Inhibition Zientek et al., Chem Res Toxicol 23: 664-676 (2010) human organic cation/carnitine transporter Diao et al., Mol Pharm, 7: 2120-2131, (2010) Volume of distribution Poulin, Ekins and Theil, Toxicol Appl Pharmacol 250: 194–212, (2011)

25. +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine

26. 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 Rhabdomyolysis or carnitine deficiency was associated with a C max/ K i value above 0.0025 (Pearson’s chi-square test p = 0.0382), N = 46. Diao et al., Mol Pharm, 7: 2120-2131, 2010 PCA used to assess training and test set overlap

27. Substrate Common feature Pharmacophore ---Used CAESAR and excluded volumes Inhibitor Hypogen pharmacophore Overlap of pharmacophores RMSD 0.27 Angstroms hOCTN2 Substrate (N10) + Inhibitor Pharmacophores Substrate pharmacophore mapped 6 out of 7 substrates in a test set. After searching ~800 known drugs, 30 were predicted to map to the substrate pharmacophore with L-carnitine shape restriction. 16 had case reports documenting an association with rhabdomyolysis Ekins et al., submitted 2011

30. How Could Green Chemistry Benefit From These Models? Chem Rev. 2010 Oct 13;110(10):5845-82 … Nature 469, 6 Jan 2011

31. … .Near Future Wider use of models New methods Free tools – need good validation studies Free databases – need to ensure structures / data are correct (DDT editorial Sept 2011) Concepts perfected on desktop may migrate to apps e.g. collaboration (MolSync+DropBox) Selective sharing of models Computational ADME/Tox mobile apps? More efficient tools Williams et al DDT in press 2011 Bunin & Ekins DDT 16: 643-645, 2011

32. scidbs.com and scimobileapps.com How do you find scientific databases, mobile Apps for science ? Development of Wiki’s to track developments in tools.. Should we do the same for ADME/Tox models?