Computational Models for                                                   Predicting Human Toxicities      Sean Ekins, M....
Outline• Key enablers• What has been modeled – a quick review• What will be modeled• Future
Why Use Computational Models For Toxicology?Goal of a model – Alert you to potential toxicity, enable you tofocus efforts ...
Key enablers: Hardware is getting smaller                                                Laptop                 1930’s    ...
Key Enablers: More data available and open tools • Details • Details
What has been modeled• Physicochemical properties, LogP, logD,  Solubility, boiling point, melting point• QSAR for various...
Physicochemical properties• Solubility data – 1000’s data in Literature• Models median error ~0.5 log = experimental error...
Simple Rules•   Rule of 5•   Lipinski, Lombardo, Dominy, Feeney Adv. Drug Deliv. Rev. 23: 3-25 (1997).•   AlogP98 vs PSA• ...
MetaPrint 2D in Bioclipse- free metabolism site predictorUses fingerprintdescriptors andmetabolitedatabase to learnfrequen...
QSAR for Various Proteins• Enzymes – predominantly Cytochrome  P450s - for drug-drug interactions• Transporters – predomin...
Pharmacophores                                                       CYP2B6Ideal when we have few molecules for training  ...
hOCTN2 – Organic Cation transporter                Pharmacophore•   High affinity cation/carnitine transporter - expressed...
hOCTN2 – Organic Cation transporter               PharmacophoreDiao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
• QSAR ExamplesGupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
• Examples – P-gp Open source descriptors CDK and C5.0 algorithm ~60,000 molecules with P-gp efflux data from Pfizer MDR <...
Time dependent inhibition for P450 3A4•   Pfizer generated a large dataset (~2000 compounds) and went through sequential B...
• 3A4 TDIIndazole ring, the pyrazole,and the methoxy-aminopyridine rings areimportant for TDIApproach decreased invitro sc...
• Drug Induced Liver Injury Models•   74 compounds - classification models (linear discriminant analysis, artificial neura...
• DILI Model - Bayesian•   Laplacian-corrected Bayesian classifier models were generated using Discovery    Studio (versio...
• DILI Bayesian   Features in DILI +                      Features in DILI -Avoid===Long aliphatic chains, Phenols, Ketone...
Test set analysis•    compounds of most interest      – well known hepatotoxic drugs (U.S. Food and Drug Administration   ...
What will be modeled• Mitochondrial toxicity, hepatotoxicity,• More Transporters – MATE, OATPs, BSEP..bigger datasets – dr...
….Near FutureWider use of modelsNew methodsFree tools – need good validation studiesFree databases – need to ensure struct...
Acknowledgments•   University of Maryland     – Lei Diao     – James E. Polli•   Pfizer     –   Rishi Gupta     –   Eric G...
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SOT short course on computational toxicology

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Presentation given as part of continuing education session at Society of Toxicology 2012.

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SOT short course on computational toxicology

  1. 1. Computational Models for Predicting Human Toxicities 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 ekinssean@yahoo.com
  2. 2. Outline• Key enablers• What has been modeled – a quick review• What will be modeled• Future
  3. 3. Why Use Computational Models For Toxicology?Goal of a model – Alert you to potential toxicity, enable you tofocus efforts on best molecules – reduce riskSelection of model – trade off between interpretability,insights for modifying molecules, speed of calculation andcoverage of chemistry space – applicability domainModels can be built with proprietary, open and commercialtoolssoftware (descriptors + algorithms) + data = model/sHuman operator decides whether a model is acceptable
  4. 4. Key enablers: Hardware is getting smaller Laptop 1930’s Room size Netbook 1980s Phone Desktop size Watch 1990s Not to scale and not equivalent computing power – illustrates mobility
  5. 5. Key Enablers: More data available and open tools • Details • Details
  6. 6. What has been modeled• Physicochemical properties, LogP, logD, Solubility, boiling point, melting point• QSAR for various proteins, complex properties• Homology models, Docking• Expert systems• Hybrid methods – combine different approaches• Mutagenicity (Ames, micronucleus, clastogenicity, and DNA damage, developmental tox.. )• Environmental Tox – Aquatic, dermatotoxicology• Mixtures – using PBPK
  7. 7. Physicochemical properties• Solubility data – 1000’s data in Literature• Models median error ~0.5 log = experimental error• LogP –tens of 1000’s data available• Fragmental or whole molecule predictors• All logP predictors are not equal. Median error ~ 0.3 log = experimental error• People now accept solubility and LogP predictions as if realACD predictions + EpiSuitepredictions in • Mobile molecular datawww.chemspider.com sheet • Links to melting point predictor from open notebook science • Required curation of data
  8. 8. Simple Rules• Rule of 5• Lipinski, Lombardo, Dominy, Feeney Adv. Drug Deliv. Rev. 23: 3-25 (1997).• AlogP98 vs PSA• Egan, Merz, Baldwin, J. Med. Chem. 43: 3867-3877 (2000)• Greater than ten rotatable bonds correlates with decreased rat oral bioavailability• Veber, Johnson, Cheng, Smith, Ward, Kopple. J Med Chem 45: 2515–2623, (2002)• Compounds with ClogP < 3 and total polar surface area > 75A2 fewer animal toxicity findings.• Hughes, et al. Bioorg Med Chem Lett 18, 4872-4875 (2008).
  9. 9. MetaPrint 2D in Bioclipse- free metabolism site predictorUses fingerprintdescriptors andmetabolitedatabase to learnfrequencies ofmetabolites invarioussubstructures L. Carlsson,et al., BMC Bioinformatics 2010, 11:362
  10. 10. QSAR for Various Proteins• Enzymes – predominantly Cytochrome P450s - for drug-drug interactions• Transporters – predominantly P-gp but some others e.g. OATP, BCRP -• Receptors – PXR, CAR, for hepatotoxicity• Ion Channels – predominantly hERG for cardiotoxicity• Issues – initially small training sets – public data is a fraction of what drug companies have
  11. 11. Pharmacophores CYP2B6Ideal when we have few molecules for training CYP2C9 CYP2D6In silico database searching CYP3A4 CYP3A5Accelrys Catalyst in Discovery Studio CYP3A7 hERGGeometric arrangement of functional groups necessary P-gp OATPsfor a biological response OCT1 OCT2•Generate 3D conformations BCRP•Align molecules hOCTN2•Select features contributing to activity ASBT•Regress hypothesis hPEPT1•Evaluate with new molecules hPEPT2 FXR LXR•Excluded volumes – relate to inactive molecules CAR PXR etc
  12. 12. hOCTN2 – Organic Cation transporter Pharmacophore• High affinity cation/carnitine transporter - expressed in kidney, skeletal muscle, heart, placenta and small intestine• Inhibition correlation with muscle weakness - rhabdomyolysis• A common features pharmacophore developed with 7 inhibitors• Searched a database of over 600 FDA approved drugs - selected drugs for in vitro testing.• 33 tested drugs predicted to map to the pharmacophore, 27 inhibited hOCTN2 in vitro• Compounds were more likely to cause rhabdomyolysis if the Cmax/Ki ratio was higher than 0.0025 Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
  13. 13. hOCTN2 – Organic Cation transporter PharmacophoreDiao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
  14. 14. • QSAR ExamplesGupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
  15. 15. • Examples – P-gp Open source descriptors CDK and C5.0 algorithm ~60,000 molecules with P-gp efflux data from Pfizer MDR <2.5 (low risk) (N = 14,175) MDR > 2.5 (high risk) (N = 10,820) Test set MDR <2.5 (N = 10,441) > 2.5 (N = 7972) CDK +fragment descriptors MOE 2D +fragment descriptors Kappa 0.65 0.67sensitivity 0.86 0.86specificity 0.78 0.8 PPV 0.84 0.84 Could facilitate model sharing?Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
  16. 16. Time dependent inhibition for P450 3A4• Pfizer generated a large dataset (~2000 compounds) and went through sequential Bayesian model generation and testing cycles Test set 2 20 active in 156 compounds Combined both model predictions Zientek et al., Chem Res Toxicol 23: 664-676 (2010)
  17. 17. • 3A4 TDIIndazole ring, the pyrazole,and the methoxy-aminopyridine rings areimportant for TDIApproach decreased invitro screening 30%Helps identify reactivemetabolite formingcompoundsZientek et al., Chem Res Toxicol 23: 664-676 (2010)
  18. 18. • Drug Induced Liver Injury Models• 74 compounds - classification models (linear discriminant analysis, artificial neural networks, and machine learning algorithms (OneR)) – Internal cross-validation (accuracy 84%, sensitivity 78%, and specificity 90%). Testing on 6 and 13 compounds, respectively > 80% accuracy. (Cruz-Monteagudo et al., J Comput Chem 29: 533-549, 2008).• A second study used binary QSAR (248 active and 283 inactive) Support vector machine models – – external 5-fold cross-validation procedures and 78% accuracy for a set of 18 compounds (Fourches et al., Chem Res Toxicol 23: 171-183, 2010).• A third study created a knowledge base with structural alerts from 1266 chemicals. – Alerts created were used to predict results for 626 Pfizer compounds (sensitivity of 46%, specificity of 73%, and concordance of 56% for the latest version) (Greene et al., Chem Res Toxicol 23: 1215-1222, 2010).
  19. 19. • DILI Model - Bayesian• Laplacian-corrected Bayesian classifier models were generated using Discovery Studio (version 2.5.5; Accelrys).• Training set = 295, test set = 237 compounds• Uses two-dimensional descriptors to distinguish between compounds that are DILI-positive and those that are DILI-negative – ALogP – ECFC_6 – Apol – – logD molecular weight Extended – number of aromatic rings connectivity – number of hydrogen bond acceptors – number of hydrogen bond donors fingerprints – number of rings – number of rotatable bonds – molecular polar surface area – molecular surface area – Wiener and Zagreb indicesEkins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
  20. 20. • DILI Bayesian Features in DILI + Features in DILI -Avoid===Long aliphatic chains, Phenols, Ketones, Diols, α-methyl styrene, Conjugated structures, Cyclohexenones, Amides
  21. 21. Test set analysis• compounds of most interest – 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. Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
  22. 22. What will be modeled• Mitochondrial toxicity, hepatotoxicity,• More Transporters – MATE, OATPs, BSEP..bigger datasets – driven by academia• Screening centers – more data – more models• Understanding differences between ligands for Nuclear Receptors – CAR vs PXR• Models will become replacements for data as datasets expand (e.g. like logP)• Toxicity Models used for Green Chemistry Chem Rev. 2010 Oct 13;110(10):5845-82
  23. 23. ….Near FutureWider use of modelsNew methodsFree tools – need good validation studiesFree databases – need to ensure structures / data are correct (DDT editorial Sept2011)Concepts perfected on desktop may migrate to apps e.g. collaboration(MolSync+DropBox) Selective sharing of modelsComputational ADME/Tox mobile apps?More efficient tools Williams et al DDT in press 2011 Bunin & Ekins DDT 16: 643-645, 2011
  24. 24. Acknowledgments• University of Maryland – Lei Diao – James E. Polli• Pfizer – Rishi Gupta – Eric Gifford – Ted Liston – Chris Waller• Merck – Jim Xu• Antony J. Williams (RSC)• Accelrys• CDD• Email: ekinssean@yahoo.com• Slideshare: http://www.slideshare.net/ekinssean• Twitter: collabchem• Blog: http://www.collabchem.com/• Website: http://www.collaborations.com/CHEMISTRY.HTM

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