Nc state lecture v2 Computational Toxicology

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 [email_address] Computational Approaches to Toxicology

… mathematical learning will be the distinguishing mark of a physician from a quack… Richard Mead A mechanical account of poisons in several essays 2nd Edition, London, 1708.

Key enablers What has been modeled – a quick review How models can be used - applications What will be modeled Future Outline

Computational toxicology is a broad term. It is also known as in silico toxicology, predictive toxicology. ‘ anything that you can do with a computer in toxicology.’ QSAR = quantitative structure activity relationship Definitions

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. Ekins et al., Trends Pharm Sci 26: 202-209 (2005)

3Rs Call for Reduced Animal Testing Cost effective Obtain new information that is not available using traditional methods Rapid Identifies toxicity early on Less time consuming than testing Legislation REACH Domestic Substances List in Canada Chemical Substances Control List in Japan Also interest in applying models to green chemistry Why Should I use in silico Tools?

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

In silico tools Information retrieved or predicted Databases Records of toxicological information Calculation of physio-chemical descriptors Various physiochemical properties Calculation of chemical structure-based properties 2-D and molecular orbital properties Calculation of toxicological effects – direct prediction of endpoints Structural based expert systems Multivariate based QSAR systems Grouping or category approach

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

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

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

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

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 > 75A 2 fewer animal toxicity findings. Hughes, et al. Bioorg Med Chem Lett 18, 4872-4875 (2008).

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

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 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 CYP2B6 CYP2C9 CYP2D6 CYP3A4 CYP3A5 CYP3A7 hERG P-gp OATPs OCT1 OCT2 BCRP hOCTN2 ASBT hPEPT1 hPEPT2 FXR LXR CAR PXR etc

Interaction between hyperforin in St Johns Wort and irinotecan = reduces efficacy Ablating the inflammatory response mediated by exogenous toxins e.g. inflammatory diseases of the bowel Cholesterol metabolism pathway control - a negative effect Mediating blood-brain barrier efflux of drugs modulation of efflux transporters e.g. mdr1 and mrp2. Decrease retention of CNS drugs e.g. anti-epileptics and pain killers, decreasing efficacy PXR induces cell growth and is pro-carcinogenic Growing role for PXR agonists

DNA binding domains have high amino acid identity but LBD are divergent Species dependent effects on transporter and enzyme induction is due to activation of PXR and other NHRs Species differences in PXR and mouse, rabbit, zebrafish, chicken… Species differences in Rifampin agonism Human, monkey, chicken, dog & Rabbit but not rat or mouse PCN - rat but not human

* * Maximum likelihood NHR phylogeny Ekins et al., BMC Evol Biol. 8(1):103 (2008) * * * * *

Pharmacophore Models for PXR Evolution Diversity of ligands can be useful for characterization 16 molecules tested in 6 species initially – HepG2 luciferase-based reporter gene assay generated EC 50 data Murideoxycholic acid Chenodeoxycholic acid Deoxycholic acid Lithocholic acid Cholic acid 5b-cholestan-3a,7a,12a-triol 5b-sycmnol sulphate 5a-cyprinol sulfate 3a,7a,12a-trihydroy-5b-cholestan-27-oic acid taurine conjugate Tauro-b-muricholic acid 7a-hydroxycholesterol 5b-pregnane-3,20-dione benzo[a]pyrene N-butyl-p-aminobenzoate Nifedipine TCDD Upto 4 excluded volumes Ekins et al., BMC Evol Biol 8(1):103 (2008)

Human r=0.7 Zebrafish r=0.8 Mouse r=0.8 Rabbit r=0.8 Chicken r=0.7 TCDD (green) and 5  -pregnane-3,20-dione (grey) Ekins et al., BMC Evol Biol 8(1):103 (2008) Pharmacophores show PXR evolution Rat r=0.7

Ciona (Sea Squirt) VDR/PXR pharmacophore 6-formylindolo-[3,2- b ]carbazole was aligned with carbamazepine and n -butyl- p aminobenzoate Suggests planar binding site Ligand selectivity is surprisingly species dependent Undergone an ever expanding role in evolution from prechordates to fish to mammals and birds Ekins et al., BMC Evol Biol. 2;8(1):103 (2008) TCDD = 0.23  M Reschly et al BMC Evol Biol 7:222 (2007)

Pharmacophores, nuclear receptors and evolution

Statistical Methodologies Non Linear regression Genetic algorithms Neural networks Support vector machines Recursive partitioning (trees) Sammon maps Bayesian methods Kohonen maps A rich collection of descriptors. Public and proprietary data. Problems to date – small datasets Understanding applicability chemical space Tools for big datasets P-gp +ve P-gp -ve Balakin et al.,Curr Drug Disc Technol 2:99-113, 2005. Ivanenkov, et al., Drug Disc Today, 14: 767-775, 2009.

Drug induced liver injury DILI Drug metabolism in the liver can convert some drugs into highly reactive intermediates, In turn can adversely affect the structure and functions of the liver. DILI, is the number one reason drugs are not approved and also the reason some of them were withdrawn from the market after approval Estimated global annual incidence rate of DILI is 13.9-24.0 per 100,000 inhabitants, and DILI accounts for an estimated 3-9% of all adverse drug reactions reported to health authorities Herbal components can cause DILI too https://dilin.dcri.duke.edu/for-researchers/info/

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 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

DILI Bayesian Features in DILI - Features in DILI + Avoid===Long aliphatic chains, Phenols, Ketones, Diols,  -methyl styrene, Conjugated structures, Cyclohexenones, Amides

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.

Fingolimod (Gilenya) for MS (EMEA and FDA) Paliperidone for schizophrenia Pirfenidone for Idiopathic pulmonary fibrosis Roflumilast for pulmonary disease Predictions for newly approved EMEA compounds Can we get DILI data for these?

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 C max / K i ratio was higher than 0.0025 Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

hOCTN2 – Organic Cation transporter Pharmacophore Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009) +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine

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 Diao et al., Mol Pharm, 7: 2120-2131, 2010 PCA used to assess training and test set overlap

Among the 21 drugs associated with rhabdomyolysis or carnitine deficiency, 14 (66.7%) provided a C max/ K i ratio higher than 0.0025. Among 25 drugs that were not associated with rhabdomyolysis or carnitine deficiency, only 9 (36.0%) showed a C max / K i ratio higher than 0.0025. Rhabdomyolysis or carnitine deficiency was associated with a C max / K i value above 0.0025 (Pearson’s chi-square test p = 0.0382). limitations of C max / K i serving as a predictor for rhabdomyolysis -- C max / K i does not consider the effects of drug tissue distribution or plasma protein binding. hOCTN2 association with rhabdomyolysis

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.

The big idea Challenge..There is limited access to ADME/Tox data and models needed for R&D How could a company share data but keep the structures proprietary? Sharing models means both parties use costly software What about open source tools? Pfizer had never considered this - So we proposed a study and Rishi Gupta generated models

Pfizer Open models and descriptors Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010 What can be developed with very large training and test sets? HLM training 50,000 testing 25,000 molecules training 194,000 and testing 39,000 MDCK training 25,000 testing 25,000 MDR training 25,000 testing 18,400 Open molecular descriptors / models vs commercial descriptors

Examples – Metabolic Stability Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010 PCA of training (red) and test (blue) compounds Overlap in Chemistry space HLM Model with CDK and SMARTS Keys: HLM Model with MOE2D and SMARTS Keys # Descriptors: 578 Descriptors # Training Set compounds: 193,650 Cross Validation Results: 38,730 compounds Training R 2 : 0.79 20% Test Set R 2 : 0.69 Blind Data Set (2310 compounds): R 2 = 0.53 RMSE = 0.367 Continuous  Categorical: κ = 0.40 Sensitivity = 0.16 Specificity = 0.99 PPV = 0.80 Time (sec/compound): 0.252 # Descriptors: 818 Descriptors # Training Set compounds: 193,930 Cross Validation Results: 38,786 compounds Training R 2 : 0.77 20% Test Set R 2 : 0.69 Blind Data Set (2310 compounds): R 2 = 0.53 RMSE = 0.367 Continuous  Categorical: κ = 0.42 Sensitivity = 0.24 Specificity = 0.987 PPV = 0.823 Time (sec/compound): 0.303

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?

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

Ekins et al., Trends Pharm Sci 26: 202-209 (2005) Converging Technologies Ekins et al., Trends Pharm Sci 26: 202-209 (2005)

PathwayStudio Pathway / Network/ Database Software Available Ekins et al., in High Content Screening , Eds. Giuliano, Taylor & Haskin (2006)

Network of genes from rat liver slices incubated with 2.5 mM Acetaminophen for 3 hours Olinga et al, Drug Metab Rev: 39, S1, 1-388, 2007 . Fibrotic response seen at 3h Mimics in vivo Transcription Regulator Enzyme Group or Complex Kinase Red = up regulated, Green = down regulated Transcription Regulator Enzyme Group or Complex Kinase

Human PXR – direct downstream interactions PXR increases transcription of CYP3A4 and >37 other genes Transporters, drug metabolizing enzymes

Measure Xu JJ, Ekins S, McGlashen M and Lauffenburger D, in Ekins S and Xu JJ, Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools, P351-379, 2009. 4M Systems Biology Manipulate Model Mine

Make science more accessible = >communication Mobile – take a phone into field /lab and do science more readily than on a laptop GREEN – energy efficient computing MolSync + DropBox + MMDS = Share molecules as SDF files on the cloud = collaborate Mobile Apps for Drug Discovery Williams et al DDT 16:928-939, 2011

Green Solvents App Bad Good www.scimobileapps.com

Mobile Apps for Drug Discovery Clark et al., submitted 2011

Future: 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

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

Acknowledgments Sneha Bhatia RIFM Lei Diao & James E. Polli University of Maryland Rishi Gupta, Eric Gifford,Ted Liston, Chris Waller – pfizer Jim Xu – Merck Matthew D. Krasowski , Erica J. Reschly, Manisha Iyer, (University of Iowa) Seth Kullman et al: (NC State) Andrew Fidler (NZ) Sandhya Kortagere (Drexel University) Peter Olinga (Groningen University) Dana Abramowitz (Ingenuity) Antony J. Williams (RSC) Alex Clark Accelrys CDD Ingenuity Email: ekinssean@yahoo.com Slideshare: http://www.slideshare.net/ekinssean Twitter: collabchem Blog: http://www.collabchem.com/ Website: http://www.collaborations.com/CHEMISTRY.HTM

Nc state lecture v2 Computational Toxicology

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