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Finland Helsinki Drug Research slides 2011

Slides presented 19 th Sept 2011 in Helsinki

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Finland Helsinki Drug Research slides 2011

  1. 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. 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. 3. The future: crowdsourced drug discovery Williams et al., Drug Discovery World, Winter 2009
  4. 4. <ul><li>Lipophilicity (log P, log D), pK a </li></ul><ul><li>Solubility </li></ul><ul><li>Passive permeability (BBB, gut, ...) </li></ul><ul><li>Plasma protein binding </li></ul><ul><li>Affinity for transporters (P-gp, hOCT, BCRP) </li></ul><ul><li>Nature of metabolites </li></ul><ul><li>Toxicity endpoints (mutagenesis, cytotoxicity, ...) </li></ul><ul><li>CL H , CL R , CL int </li></ul><ul><li>V D , t 1/2 , ... </li></ul>composite character What has been modeled
  5. 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. 6. Models and software becoming more accessible- free, precompetitive efforts - collaboration
  7. 7. L. Carlsson,et al., BMC Bioinformatics 2010, 11: 362 MetaPrint 2D in Bioclipse- free metabolism site predictor
  8. 8. The reality for most <ul><li>Little data </li></ul><ul><li>Where are ADME/Tox models? </li></ul><ul><li>What tools are out there? </li></ul><ul><li>Who can do the modeling? </li></ul><ul><li>What do the outputs mean? </li></ul>
  9. 9. Simple Rules <ul><li>Rule of 5 </li></ul><ul><li>Lipinski, Lombardo, Dominy, Feeney Adv. Drug Deliv. Rev. 23: 3-25 (1997). </li></ul><ul><li>AlogP98 vs PSA </li></ul><ul><li>Egan, Merz, Baldwin, J. Med. Chem. 43: 3867-3877 (2000) </li></ul><ul><li>Greater than ten rotatable bonds correlates with decreased rat oral bioavailability </li></ul><ul><li>Veber, Johnson, Cheng, Smith, Ward, Kopple. J Med Chem 45: 2515–2623, (2002) </li></ul><ul><li>Compounds with ClogP < 3 and total polar surface area > 75A 2 fewer animal toxicity findings. </li></ul><ul><li>Hughes, et al. Bioorg Med Chem Lett 18 , 4872-4875 (2008). </li></ul>
  10. 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
  11. 11. <ul><li>“ There have been at least 40 studies published since 1988 which we have summarized (1,2) that derive various quantitative structure activity relationship (QSAR) or classification models for BBB penetration data.” </li></ul><ul><li>the performance of the best models in both studies appear remarkably congruent. </li></ul><ul><li>prediction accuracies of 80 – 83% for the 10 fold cross validation and could correctly predict 84% of BBB+ drugs. </li></ul><ul><li>The k-NN MOE model had an accuracy of 82% for the 99 compound test set and the k-NN Dragon model had an accuracy of 100% with the applicability domain. </li></ul>
  12. 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.
  13. 13. <ul><li>What can be developed with very large training and test sets? </li></ul><ul><li>HLM training 50,000 testing 25,000 molecules </li></ul><ul><li>training 194,000 and testing 39,000 </li></ul><ul><li>MDCK training 25,000 testing 25,000 </li></ul><ul><li>MDR training 25,000 testing 18,400 </li></ul><ul><li>Open molecular descriptors / models vs commercial descriptors </li></ul>Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010 Open source tools for modeling
  14. 14. Massive Human liver microsomal stability model PCA of training (red) and test (blue) compounds Overlap in Chemistry space Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010 <ul><li># Descriptors: 818 Descriptors </li></ul><ul><li># Training Set compounds: 193,930 </li></ul><ul><li>Cross Validation Results: 38,786 compounds </li></ul><ul><li>Training R 2 : 0.77 </li></ul><ul><li>20% Test Set R 2 : 0.69 </li></ul><ul><li>Blind Data Set (2310 compounds): </li></ul><ul><li>R 2 = 0.53 </li></ul><ul><li>RMSE = 0.367 </li></ul><ul><li>Continuous  Categorical: </li></ul><ul><li>κ = 0.42 </li></ul><ul><li>Sensitivity = 0.24 </li></ul><ul><li>Specificity = 0.987 </li></ul><ul><li>PPV = 0.823 </li></ul><ul><li>Time (sec/compound): 0.303 </li></ul><ul><li># Descriptors: 578 Descriptors </li></ul><ul><li># Training Set compounds: 193,650 </li></ul><ul><li>Cross Validation Results: 38,730 compounds </li></ul><ul><li>Training R 2 : 0.79 </li></ul><ul><li>20% Test Set R 2 : 0.69 </li></ul><ul><li>Blind Data Set (2310 compounds): </li></ul><ul><li>R 2 = 0.53 </li></ul><ul><li>RMSE = 0.367 </li></ul><ul><li>Continuous  Categorical: </li></ul><ul><li>κ = 0.40 </li></ul><ul><li>Sensitivity = 0.16 </li></ul><ul><li>Specificity = 0.99 </li></ul><ul><li>PPV = 0.80 </li></ul><ul><li>Time (sec/compound): 0.252 </li></ul>HLM Model with MOE2D and SMARTS Keys HLM Model with CDK and SMARTS Keys:
  15. 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. 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
  17. 17. Application : Drug induced liver injury DILI <ul><li>Drug metabolism in the liver can convert some drugs into highly reactive intermediates, </li></ul><ul><li>In turn can adversely affect the structure and functions of the liver. </li></ul><ul><li>DILI, is the number one reason drugs are not approved </li></ul><ul><ul><li>and also the reason some of them were withdrawn from the market after approval </li></ul></ul><ul><li>Estimated global annual incidence rate of DILI is 13.9-24.0 per 100,000 inhabitants, </li></ul><ul><ul><li>and DILI accounts for an estimated 3-9% of all adverse drug reactions reported to health authorities </li></ul></ul><ul><li>Herbal components can cause DILI too </li></ul>
  18. 18. DILI data <ul><li>Tested a panel of orally administered drugs at multiples of the maximum therapeutic concentration ( C max ), </li></ul><ul><ul><li>taking into account the first-pass effect of the liver and other idiosyncratic toxicokinetic/toxicodynamic factors. </li></ul></ul><ul><li>The 100-fold C max scaling factor represented a reasonable threshold to differentiate safe versus toxic drugs for an orally dosed drug and with regard to hepatotoxicity. </li></ul><ul><li>Concordance of the in vitro human hepatocyte imaging assay technology (HIAT) for 300 drugs and chemicals, ~ 75% with regard to clinical hepatotoxicity, with very few false-positive results </li></ul><ul><li>Xu et al., Toxicol Sci 105: 97-105 (2008). </li></ul><ul><li>2 models and 1 rule based method published – small test sets </li></ul><ul><li>Cruz-Monteagudo et al., J Comput Chem 29: 533-549, 2008 </li></ul><ul><li>Fourches et al., Chem Res Toxicol 23: 171-183, 2010 </li></ul><ul><li>Greene et al., Chem Res Toxicol 23: 1215-1222, 2010 </li></ul>
  19. 19. Bayesian machine learning <ul><li>Laplacian-corrected Bayesian classifier models were generated using Discovery Studio (version 2.5.5; Accelrys). </li></ul><ul><li>Training set = 295, test set = 237 compounds </li></ul><ul><li>Uses two-dimensional descriptors to distinguish between compounds that are DILI-positive and those that are DILI-negative </li></ul><ul><ul><li>ALogP </li></ul></ul><ul><ul><li>ECFC_6 </li></ul></ul><ul><ul><li>Apol </li></ul></ul><ul><ul><li>logD </li></ul></ul><ul><ul><li>molecular weight </li></ul></ul><ul><ul><li>number of aromatic rings </li></ul></ul><ul><ul><li>number of hydrogen bond acceptors </li></ul></ul><ul><ul><li>number of hydrogen bond donors </li></ul></ul><ul><ul><li>number of rings </li></ul></ul><ul><ul><li>number of rotatable bonds </li></ul></ul><ul><ul><li>molecular polar surface area </li></ul></ul><ul><ul><li>molecular surface area </li></ul></ul><ul><ul><li>Wiener and Zagreb indices </li></ul></ul>Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Extended connectivity fingerprints
  20. 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
  21. 21. Test set analysis <ul><li>compounds of most interest </li></ul><ul><ul><li>well known hepatotoxic drugs (U.S. Food and Drug Administration Guidance for Industry “Drug-Induced Liver Injury: Premarketing Clinical Evaluation,” 2009), plus their less hepatotoxic comparators, if clinically available. </li></ul></ul>Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Palperidone – score 8.79 max similarity 0.86 Will it cause DILI?
  22. 22. Conclusions <ul><li>First large-scale testing of DILI machine learning model </li></ul><ul><ul><li>Concordance lower than with in vitro model </li></ul></ul><ul><ul><li>Statistics similar to Structural alerts from Pfizer paper </li></ul></ul><ul><li>Could use models to filter compounds for further testing in vitro </li></ul><ul><ul><li>Use published knowledge to predict DILI </li></ul></ul><ul><ul><li>Combinations of models </li></ul></ul><ul><ul><li>Combine datasets – create models with Open descriptors and algorithms </li></ul></ul><ul><li>Make models widely available </li></ul>Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
  23. 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)
  24. 24. hOCTN2 – Organic Cation transporter <ul><li>High affinity cation/carnitine transporter - expressed in kidney, skeletal muscle, heart, placenta and small intestine </li></ul><ul><li>Inhibition correlation with muscle weakness - rhabdomyolysis </li></ul><ul><li>A common features pharmacophore developed with 7 inhibitors </li></ul><ul><li>Searched a database of over 600 FDA approved drugs - selected drugs for in vitro testing. </li></ul><ul><li>33 tested drugs predicted to map to the pharmacophore, 27 inhibited hOCTN2 in vitro </li></ul><ul><li>Compounds were more likely to cause rhabdomyolysis if the C max / K i ratio was higher than 0.0025 </li></ul>Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
  25. 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. 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. 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
  28. 28. ToxCast: comparing human PXR models <ul><li>Generated a Bayesian model with > 100 steroidal PXR agonists and non agonists </li></ul>Ekins S et al, PLoS Comp Biol 5: e1000594 (2009). A C T I V E I N A C T I V E Ekins S et al, PLoS Comp Biol 5: e1000594 (2009).
  29. 29. ToxCast (blue) vs Steroidal (yellow) compounds <ul><li>Steroid based PXR model did not predict ToxCast compounds </li></ul><ul><li>Different areas in PCA using simple descriptors </li></ul><ul><li>ToxCast data requires a model built with similar molecules or more diverse structures than steroids </li></ul><ul><li>Phase II of ToxCast – further testing of models </li></ul>Kortagere et al., Env Health Perspect, 118: 1412-1417, 2010 10 Groups have contracts with EPA to test ~300 conazoles & pesticides, etc with 400 biological assays (cell based, receptor etc)
  30. 30. How Could Green Chemistry Benefit From These Models? Chem Rev. 2010 Oct 13;110(10):5845-82 … Nature 469, 6 Jan 2011
  31. 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. 32. and 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?
  33. 33. Acknowledgments <ul><li>Pfizer </li></ul><ul><ul><li>Rishi Gupta </li></ul></ul><ul><ul><li>Eric Gifford </li></ul></ul><ul><ul><li>Ted Liston </li></ul></ul><ul><ul><li>Chris Waller </li></ul></ul><ul><li>Merck </li></ul><ul><ul><li>Jim Xu </li></ul></ul><ul><li>Antony J. Williams (RSC) </li></ul><ul><li>University of Maryland </li></ul><ul><ul><li>Lei Diao </li></ul></ul><ul><ul><li>James E. Polli </li></ul></ul><ul><li>Matthew D. Krasowski, Erica J. Reschly (University of Iowa) </li></ul><ul><li>Sandhya Kortagere (Drexel University) </li></ul><ul><li>Sridhar Mani (Albert Einstein) </li></ul><ul><li>Accelrys </li></ul><ul><li>CDD – Barry Bunin </li></ul><ul><li>Email: </li></ul><ul><li>Slideshare: </li></ul><ul><li>Twitter: collabchem </li></ul><ul><li>Blog: </li></ul><ul><li>Website: </li></ul>