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

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

2. A LITTLE BACKGROUND : computer aided drug design
1999
Accelrys UGM 2003

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

4. 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. Models and software becoming more accessible-
free

6. Driving change
Pharma reached a productivity tipping point
Cost of drug development high
Failure in clinic due to toxicity
Initiatives like REACH, ToxCast etc need to screen many molecules
Reduce use of animals
How to predict failure earlier – are we at a turning point?

7. 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

8. 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 and also the reason some of them were withdrawn from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/

9. Drug Examples for DILI + and -
Troglitazone DILI + Pioglitazone DILI - Rosiglitzone DILI -
Aspirin DILI -
Sulindac DILI + Diclofenac DILI +
Xu et al., Toxicol Sci 105: 97-105 (2008)

10. 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

11. 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).

12. 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).

13. 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
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

14. Features in DILI +
Avoid
Long aliphatic chains
Phenols
Ketones
Diols
α-methyl styrene
Conjugated structures
Cyclohexenones
Amides
?
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

15. Features in DILI -
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

16. 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

17. 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

18. Training vs test set PCA
Yellow = test
Blue = training
Retinyl
palmitate
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

19. 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

20. Predictions for newly approved EMEA compounds
Fingolimod (Gilenya) for Pirfenidone for
MS (EMEA and FDA) Idiopathic pulmonary
fibrosis
Roflumilast for
Paliperidone for pulmonary disease
schizophrenia
Name DILI Bayesian ECFC6 Bayes ian ECFC6 for paper#PredictionECFC6 for paper_ClosestSimilarity
DILI for paper DILI Bayesian
fingolimod 0.422051 TRUE 0.4
paliperidone 8.79189 TRUE 0.865385
perfenidone 0.542769 TRUE 0.322581
roflumilast 3.17631 TRUE 0.326923
Can we get DILI data for these?

21. 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

22. 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 compounds
Combined both model predictions
Zientek et al., Chem Res Toxicol 23: 664-676 (2010)

23. Important substructures for CYP3A4 Time dependent inhibition
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)

24. Pharmacophores applied broadly
Created for
Ideal when we have few molecules for training CYP2B6
In silico database searching CYP2C9
CYP2D6
Accelrys Catalyst in Discovery Studio CYP3A4
CYP3A5
CYP3A7
Geometric arrangement of functional groups necessary hERG
for a biological response P-gp
OATPs
•Generate 3D conformations OCT1
OCT2
•Align molecules
BCRP
•Select features contributing to activity hOCTN2
•Regress hypothesis ASBT
•Evaluate with new molecules hPEPT1
hPEPT2
•Excluded volumes – relate to inactive molecules FXR
LXR
CAR
PXR etc

25. 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)

26. Possible Association between Clinical Rhabdomyolysis and hOCTN2 Inhibition
Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

27. hOCTN2 quantitative pharmacophore and Bayesian model
vinblastine
cetirizine +ve
emetine
-ve
r = 0.89 Diao et al., Mol Pharm, 7: 2120-2131, 2010

28. 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
PCA used to assess
training and test set overlap
Diao et al., Mol Pharm, 7: 2120-2131, 2010

29. hOCTN2 association with rhabdomyolysis
Among the 21 drugs associated with rhabdomyolysis or carnitine
deficiency, 14 (66.7%) provided a Cmax/Ki ratio higher than
0.0025.
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 distribution
or plasma protein binding.
Diao et al., Mol Pharm, 7: 2120-2131, 2010

30. 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
Ekins et al submitted 2011
Data from Polli lab (conjugates) and literature

31. hOCTN2 Substrate + Inhibitor Pharmacophores
Inhibitor Hypogen pharmacophore
Substrate Common feature Pharmacophore
---Used CAESAR and excluded volumes
Overlap of pharmacophores
RMSD 0.27 Angstroms
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

32. Growing role for PXR agonists
 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

33. ToxCast: docking chemicals in human PXR
• 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.
Kortagere et al., Env Health Perspect, 118: 1412-1417, 2010

34. 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

35. 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

36. How Could Green Chemistry Benefit From
These Models?
…
N AT U R E, 4 6 9: 6 JA N 2 0 1 1
Chem Rev. 2010 Oct 13;110(10):5845-82

37. Increasing Data & Model Access
Could all pharmas share their data as models with each other?

38. Open source tools for modeling
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

39. 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)
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

40. ….Near Future
Better & 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

41. 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: ekinssean@yahoo.com
• Slideshare: http://www.slideshare.net/ekinssean
• Twitter: collabchem
• Blog: http://www.collabchem.com/
• Website:
http://www.collaborations.com/CHEMISTRY.HTM

42. Bayesian machine learning
Bayesian classification is a simple probabilistic classification model. It is based on
Bayes’ theorem
h is the hypothesis or model
d is the observed data
p(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
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

43. 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)