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

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. Outline
• Key enablers
• What has been modeled – a quick review
• What will be modeled
• Future

3. 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

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. Key Enablers: More data available and open tools
• Details
• Details

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. 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
• Mobile molecular data
www.chemspider.com
sheet
• Links to melting point
predictor from open
notebook science
• Required curation of data

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. MetaPrint 2D in Bioclipse- free metabolism site predictor
Uses fingerprint
descriptors and
metabolite
database to learn
frequencies of
metabolites in
various
substructures
L. Carlsson,et al., BMC Bioinformatics 2010, 11:362

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. Pharmacophores
CYP2B6
Ideal when we have few molecules for training CYP2C9
CYP2D6
In silico database searching CYP3A4
CYP3A5
Accelrys Catalyst in Discovery Studio CYP3A7
hERG
Geometric arrangement of functional groups necessary P-gp
OATPs
for 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. 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. hOCTN2 – Organic Cation transporter
Pharmacophore
Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

14. • QSAR Examples
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

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 0.67
sensitivity 0.86 0.86
specificity 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. 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. • 3A4 TDI
Indazole ring, the pyrazole,
and the methoxy-
aminopyridine rings are
important for TDI
Approach decreased in
vitro screening 30%
Helps identify reactive
metabolite forming
compounds
Zientek et al., Chem Res Toxicol 23: 664-676 (2010)

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

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

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. 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. ….Near Future
Wider 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

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