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Sean Ekins Collaborations in Chemistry, Fuquay-Varina, NC. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland. Computational Models for Predicting Human Toxicities

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Alternatives to Animal testing slides conference - -Computational Models for Predicting Human Toxicities

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Page 1: Montreal 8th world congress

Sean Ekins

Collaborations in Chemistry, Fuquay-Varina, NC.

Collaborative Drug Discovery, Burlingame, CA.School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland.

Computational Models for Predicting Human Toxicities

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A LITTLE BACKGROUND : computer aided drug design

Accelrys UGM 2003

1999

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The future: crowdsourced drug discovery

Williams et al., Drug Discovery World, Winter 2009

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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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Models and software becoming more accessible- free

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Driving change

Pharma reached a productivity tipping pointCost of drug development highFailure in clinic due to toxicity

Initiatives like REACH, ToxCast etc need to screen many moleculesReduce use of animals

How to predict failure earlier – are we at a turning point?

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Examples of Models for Human Toxicities

Drug induced liver injury (DILI) Time dependent inhibition of P450 3A4 Transporters – hOCTN2 PXR and ToxCast Precompetitive pharma models

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Application : 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DILI, is the number one reason drugs are not approved and also the reason some of them were withdrawn from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/

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Drug Examples for DILI + and -

Troglitazone DILI + Pioglitazone DILI - Rosiglitzone DILI -

Sulindac DILI +Aspirin DILI -

Diclofenac DILI +

Xu et al., Toxicol Sci 105: 97-105 (2008).

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Limitations of DILI?

Compound has to physically have been made and be available for testing.

The screening system is still relatively low throughput compared with any primary screens

Whole compound or vendor libraries cannot be cost effectively screened for prioritization.

Screening system should be representative of the human organ including drug metabolism capability.

Prediction of human therapeutic Cmax is often imprecise before clinical testing in actual patients.

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

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DILI Computational 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 data

Tested a panel of orally administered drugs at multiples of the maximum therapeutic concentration (Cmax), taking into account the first-pass effect of the liver and other

idiosyncratic toxicokinetic/toxicodynamic factors.

The 100-fold Cmax scaling factor represented a reasonable threshold to differentiate safe versus toxic drugs for an orally dosed drug and with regard to hepatotoxicity.

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

Xu et al., Toxicol Sci 105: 97-105 (2008).

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Bayesian machine learning

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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Features in DILI +

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

Avoid

Long aliphatic chainsPhenolsKetones

Diols-methyl styrene

Conjugated structuresCyclohexenones

Amides?

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Features in DILI -

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

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Results

Fingerprints with high Bayesian scores that are present in many DILI compounds appeared to be reactive in nature,

Could cause time-dependent inhibition of cytochromes P450 or be precursors for metabolites that are reactive and may covalently bind to proteins.

Why are long aliphatic chains important for DILI generally hydrophobic and perhaps enabling increased

accumulation? may be hydroxylated and then form other metabolites that are in

turn reactive?

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

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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

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Training vs test set PCA

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

Yellow = testBlue = training

Retinyl palmitate

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Compare to newer drugs

Extracted small molecule drugs from 2006 to 2010 from the Prous Integrity database

Structure validation resulted in a set of 77 molecules (mean molecular weight 427.05 ± 280.31, range 94.11–1994.09)

These molecules were distributed throughout the combined training and test sets (N = 532), representative of overlap

These combined analyses suggest that the test and training sets used for the DILI model are representative of current medicinal chemistry efforts.

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

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Fingolimod (Gilenya) for MS (EMEA and FDA)

Paliperidone for schizophrenia

Pirfenidone for Idiopathic pulmonary fibrosis

Roflumilast for pulmonary disease

Name DILI Bayesian ECFC6 for paperDILI Bayesian ECFC6 for paper#PredictionDILI Bayesian ECFC6 for paper_ClosestSimilarityfingolimod 0.422051 TRUE 0.4

paliperidone 8.79189 TRUE 0.865385perfenidone 0.542769 TRUE 0.322581roflumilast 3.17631 TRUE 0.326923

Predictions for newly approved EMEA compounds

Can we get DILI data for these?

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Conclusions

First large-scale testing of DILI machine learning model Concordance lower than with in vitro model Statistics similar to Structural alerts from Pfizer paper

Could use models to filter compounds for further testing in vitro Use published knowledge to predict DILI Combinations of models Combine datasets – create models with Open descriptors

and algorithms Make models widely available

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

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Integrated in Silico-in Vitro Strategy for Addressing Cytochrome P450 3A4 Time-Dependent Inhibition

Pfizer generated a large dataset (~2000 compounds) and went through sequential Bayesian model generation and testing cycles

Test set 2 20 active in 156 compoundsCombined both model predictions

Zientek et al., Chem Res Toxicol 23: 664-676 (2010)

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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)

Important substructures for CYP3A4 Time dependent inhibition

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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

Pharmacophores applied broadly

Created for

CYP2B6CYP2C9CYP2D6CYP3A4CYP3A5CYP3A7hERGP-gpOATPsOCT1OCT2BCRPhOCTN2ASBThPEPT1hPEPT2FXR LXRCARPXR etc

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hOCTN2 – Organic Cation transporter 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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Possible Association between Clinical Rhabdomyolysis and hOCTN2 Inhibition

Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

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+ve

-ve

hOCTN2 quantitative pharmacophore and Bayesian model

Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89

vinblastine

cetirizine

emetine

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hOCTN2 quantitative pharmacophore and Bayesian model

Bayesian Model - Leaving 50% out 97 times external ROC 0.90internal 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

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Among the 21 drugs associated with rhabdomyolysis or carnitinedeficiency, 14 (66.7%) provided a Cmax/Ki ratio higher than0.0025.

Among 25 drugs that were not associated with rhabdomyolysis or

carnitine deficiency, only 9 (36.0%) showed a Cmax/Ki ratio higher than

0.0025.

Rhabdomyolysis or carnitine deficiency was associated with a Cmax/Ki

value above 0.0025 (Pearson’s chi-square test p = 0.0382).

limitations of Cmax/Ki serving as a predictor for rhabdomyolysis-- Cmax/Ki does not consider the effects of drug tissue distributionor plasma protein binding.

hOCTN2 association with rhabdomyolysis

Diao et al., Mol Pharm, 7: 2120-2131, 2010

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hOCTN2 Substrates

Substrate Km (microM)

L-carnitine 5.3

Acetyl-L-carnitine 9

Mildronate 26

Ipratropium 53

Valproyl-L-carnitine 132 ± 23

Naproxen-L-carnitine 257 ± 57

Ketoprofen-L-carnitine 77.0 ± 4.0

Ketoprofen-glycine-L-carnitine 58.5 ± 8.7

Valproyl-glycolic acid-L-carnitine 161 ± 50

Data from Polli lab (conjugates) and literature

Ekins et al submitted 2011

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Substrate Common feature Pharmacophore---Used CAESAR and excluded volumes

Inhibitor Hypogen pharmacophore

Overlap of pharmacophores RMSD 0.27 Angstroms

hOCTN2 Substrate + 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

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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

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• 10 Groups had contracts with EPA to test ~300 conazoles & pesticides, etc with various biological assays (cell based, receptor etc)

• We have docked all the molecules into the PXR agonist site of 5 structures

• GOLD (ver 4) -genetic algorithm explores conformations of ligands and flexible receptor side

• 20 independent docking runs • Used the regular goldscore to classify compounds • Comparing their respective scores to the corresponding

goldscores of the co-crystalized ligands. • Majority vote across the five structures.

ToxCast: docking chemicals in human PXR

Kortagere et al., Env Health Perspect, 118: 1412-1417, 2010

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ToxCast: docking pesticides in PXR

• Activities of most activators more potent vs NCGC data

• We correctly predict ~70% of compounds and 75% of activators

• Including other predicted pesticides from Lemaire, G et al., Toxicol Sci. 2006; 91:501-9, (2006).

• When compared to NCGC data for complete Toxcast set Sensitivity 74%

Kortagere et al., Env Health Perspect, 118: 1412-1417, 2010

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ToxCast (blue) vs Steroidal (yellow) compounds

•Different areas in PCA using simple descriptors

•ToxCast requires a model built with similar molecules

•General PXR models may be limited in predicting ToxCast data•Phase II of ToxCast – further testing of models

Kortagere et al., Env Health Perspect, 118: 1412-1417, 2010

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How Could Green Chemistry Benefit From These Models?

Chem Rev. 2010 Oct 13;110(10):5845-82

N AT U R E, 4 6 9: 6 JA N 2 0 1 1

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Could all pharmas share their data as models with each other?

Increasing Data & Model Access

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Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

Open source tools for modeling

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Open source tools for modeling

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?

Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

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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….Near FutureBetter & wider applicability domain models available

Wider use of models

Selective sharing of models

Computational ADME/Tox apps?

Williams et al DDT in pressBunin & Ekins DDT in Press

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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) Matthew D. Krasowski, Erica J. Reschly

(University of Iowa) Sandhya Kortagere (Drexel University) Sridhar Mani (Albert Einstein) 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

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Bayesian machine learning

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

Bayesian classification is a simple probabilistic classification model. It is based on Bayes’ theorem

h is the hypothesis or modeld is the observed datap(h) is the prior belief (probability of hypothesis h before observing any data)p(d) is the data evidence (marginal probability of the data)p(d|h) is the likelihood (probability of data d if hypothesis h is true) p(h|d) is the posterior probability (probability of hypothesis h being true given the observed data d)

A weight is calculated for each feature using a Laplacian-adjusted probability estimate to account for the different sampling frequencies of different features.

The weights are summed to provide a probability estimate

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Examples of using Bayesian Models

Integrated in Silico-in Vitro Strategy for Addressing Cytochrome P450 3A4 Time-Dependent Inhibition

Zientek et al., Chem Res Toxicol 23: 664-676 (2010)

Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR

Ekins S, Kortagere S, Iyer M, Reschly EJ, Lill MA, Redinbo MR and Krasowski MD, PLoS Comput Biol 5(12): e1000594, (2009) .

Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter

Zheng X, et al., Mol Pharm, 6: 1591-1603, (2009)

Quantitative structure activity relationship for inhibition of human organic cation/carnitine transporter

Diao et al., Mol Pharm, 7: 2120-2131, (2010)