SOT short course on computational toxicology
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Transcript of SOT short course on computational toxicology
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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
Computational Models for
Predicting Human Toxicities
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• Key enablers
• What has been modeled – a quick review
• What will be modeled
• Future
Outline
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Why Use Computational Models For Toxicology?
Goal of a model – Alert you to potential toxicity, enable you to focus efforts on best molecules – reduce risk
Selection of model – trade off between interpretability, insights for modifying molecules, speed of calculation and coverage of chemistry space – applicability domain
Models can be built with proprietary, open and commercial tools
software (descriptors + algorithms) + data = model/s
Human operator decides whether a model is acceptable
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Key enablers: 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
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Key Enablers: More data available and open tools
• Details
• Details
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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
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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 real
ACD predictions + EpiSuite predictions in www.chemspider.com
• Mobile molecular data sheet
• Links to melting point predictor from open notebook science
• Required curation of data
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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).
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L. Carlsson,et al., BMC Bioinformatics 2010, 11:362
MetaPrint 2D in Bioclipse- free metabolism site predictor
Uses fingerprint descriptors and metabolite database to learn frequencies of metabolites in various substructures
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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
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Pharmacophores
Ideal when we have few molecules for training In silico database searching
Accelrys Catalyst in Discovery Studio
Geometric arrangement of functional groups necessary for a biological response
•Generate 3D conformations•Align molecules•Select features contributing to activity•Regress hypothesis•Evaluate with new molecules
•Excluded volumes – relate to inactive molecules
CYP2B6CYP2C9CYP2D6CYP3A4CYP3A5CYP3A7hERGP-gpOATPsOCT1OCT2BCRPhOCTN2ASBThPEPT1hPEPT2FXR LXRCARPXR etc
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hOCTN2 – Organic Cation transporterPharmacophore
• 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)
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hOCTN2 – Organic Cation transporterPharmacophore
Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
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• QSAR Examples
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
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• Examples – P-gp
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
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)
Could facilitate model sharing?
CDK +fragment descriptors MOE 2D +fragment descriptorsKappa 0.65 0.67
sensitivity 0.86 0.86specificity 0.78 0.8
PPV 0.84 0.84
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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)
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• 3A4 TDI
Indazole ring, the pyrazole, and the methoxy-aminopyridine rings areimportant for TDI
Approach decreased in vitro screening 30%
Helps identify reactive metabolite forming compounds
Zientek et al., Chem Res Toxicol 23: 664-676 (2010)
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• 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).
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• 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 – number of aromatic rings – number of hydrogen bond acceptors – number of hydrogen bond donors – number of rings – number of rotatable bonds – molecular polar surface area – molecular surface area – Wiener and Zagreb indices
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
Extended connectivity fingerprints
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• DILI Bayesian
Features in DILI -Features in DILI +
Avoid===Long aliphatic chains, Phenols, Ketones, Diols, -methyl styrene, Conjugated structures, Cyclohexenones, Amides
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Test set analysis
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
• 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.
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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
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….Near FutureWider 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
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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: [email protected]
Slideshare: http://www.slideshare.net/ekinssean
Twitter: collabchem
Blog: http://www.collabchem.com/
Website: http://www.collaborations.com/CHEMISTRY.HTM