montreal 8th world congress
Post on 28-Jan-2015
115 Views
Preview:
DESCRIPTION
TRANSCRIPT
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
A LITTLE BACKGROUND : computer aided drug design
Accelrys UGM 2003
1999
The future: crowdsourced drug discovery
Williams et al., Drug Discovery World, Winter 2009
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
Models and software becoming more accessible- free
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?
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
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DILI, is the number one reason drugs are not approved and also the reason some of them were withdrawn from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/
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).
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
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).
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).
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
Features in DILI +
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
Avoid
Long aliphatic chainsPhenolsKetones
Diols-methyl styrene
Conjugated structuresCyclohexenones
Amides?
Features in DILI -
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
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
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
Training vs test set PCA
Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
Yellow = testBlue = training
Retinyl palmitate
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
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?
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
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)
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
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
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)
Possible Association between Clinical Rhabdomyolysis and hOCTN2 Inhibition
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 0.90internal 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
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
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
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
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
• 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
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
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
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
Could all pharmas share their data as models with each other?
Increasing Data & Model Access
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
Open source tools for modeling
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
$ $$$$$$
….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
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
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
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)
top related