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SOT short course on computational toxicology
 

SOT short course on computational toxicology

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

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

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    SOT short course on computational toxicology SOT short course on computational toxicology Presentation Transcript

    • 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
    • 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 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
    • 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
    • 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 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
    • 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
    • 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).
    • MetaPrint 2D in Bioclipse- free metabolism site predictorUses fingerprintdescriptors andmetabolitedatabase to learnfrequencies ofmetabolites invarioussubstructures L. Carlsson,et al., BMC Bioinformatics 2010, 11:362
    • 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
    • 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
    • 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)
    • 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 <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
    • 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)
    • • 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)
    • • 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).
    • • 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
    • • DILI Bayesian Features in DILI + Features in DILI -Avoid===Long aliphatic chains, Phenols, Ketones, Diols, α-methyl styrene, Conjugated structures, Cyclohexenones, Amides
    • 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
    • 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
    • ….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
    • 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