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Perspective on QSAR modeling of transport


Talk given at AAPS Workshop on drug transporters in ADME: from bench to the bedside march 14-16, 2011, Bethesda

Talk given at AAPS Workshop on drug transporters in ADME: from bench to the bedside march 14-16, 2011, Bethesda

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  • CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing),, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
  • Two P-gp digoxin models were Hypogen models and the substrate is a HIPHOP model. The selection criteria for the substrates was 1) no literature reported P-gp interaction; 2) model predicted IC50 values lower than 10  M; 3) commercial availability.


  • 1. Perspective on QSAR modeling of transport Sean Ekins Collaborations in Chemistry, Jenkintown, PA. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland. Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, NJ.
  • 2. The future: crowdsourced drug discovery Williams et al., Drug Discovery World, Winter 2009
  • 3. Why We Need Transporter Models
    • Predict if a compound will interact at a transporter
    • Define structure activity relationship( SAR)
    • Start understanding ligand-protein interactions
    • in vitro models - limited throughput & in vivo study complicated by multiple transporters with overlapping substrate specificities.
    • in silico – in vitro approach has value in targeting testing of compounds with a high probability of activity.
    • Computers can beat humans at chess and Jeopardy why not help with transporters?
  • 4. In Silico Pharmacology- decision process Ekins et al., BJP 152, 21–37 (2007)
  • 5. From one extreme to another
    • A decade ago focus was P-gp and we had small datasets
    • Now we may have massive (tens of thousands of compounds) datasets in pharma
    • Over time - Bigger datasets, more complex models + use of more methods
    • Shift in interest to other transporters – lots of them - datasets still very small or non existent
    • Predominant - inhibitor data
  • 6.
    • 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 CYP2B6 CYP2C9 CYP2D6 CYP3A4 CYP3A5 CYP3A7 hERG P-gp OATPs OCT1 OCT2 BCRP hOCTN2 ASBT hPEPT1 hPEPT2 FXR LXR CAR PXR etc
  • 7. PXR agonists CYP3A4 substrates and inhibitors P-gp substrates and inhibitors hERG blockers Hydrophobic (ring aromatic) Positive ionizable (basic Nitrogen not essential) Hydrogen bond acceptors Hydrophobic Hydrogen bond acceptors Hydrogen bond donor (occasionally) Hydrophobic (ring aromatic) Hydrogen bond acceptors Hydrogen bond donor Positive ionizable Hydrophobic (ring aromatic) Hydrogen bond acceptors Hydrogen bond donor Pharmacophore Features How to avoid interaction with the protein Attaching hydrogen bonding groups on one of the hydrophobic features, adding larger more rigid groups as well as removing central H-bond acceptors Replace metabolically labile feature with a halogen Make molecule metabolically inactive renal excreted Increase polarity (ClogP <1) Removal of one of the interactions in the hERG pharmacophore Add other groups to structure – increase bulk Decrease lipophilicity logD 7.4 Decrease number of aromatic bonds Decrease ring aromatics Decrease molecular size Ekins S and Kortagere S,, in Scheiber J and Jenkins J.L., In-silico Methods for Adverse Effect Prediction in Preclinical Drug Discovery John Wiley and Sons. Hoboken, NJ. In Press 2011
  • 8. Pharmacophore Models Substrate Model 1 (aligned with verapamil)
    • The Catalyst HIPHOP model is generated based on verapamil and digoxin.
    • 5 hydrophobic (HYD) features (cyan)
    • 2 hydrogen bond acceptor (HBA) features (green)
    Inhibitor Model 1 (aligned with LY335979)
    • The model is calculated based on 27 diverse compounds with IC 50 for inhibition of [ 3 H]-digoxin transport in Caco-2.
    • 4 HYD features (cyan)
    • 1 HBA Feature (green)
    Inhibitor Model 2 (aligned with CP114416)
    • The model is an update of above model with six additional inhibitors (FK506A, PSC 833, Ritonavir, Erythromycin, Cyclosporin and CP99542).
    • 4 HYD features (cyan)
    • 1 HBA feature (green)
    Chang, Bahadurri et al, DMD 34, 1976-1984 (2006)
  • 9. Pharmacophore Development Database screening 189 known P-gp substrates and non-substrates 576 prescription drugs. In silico validation In vitro validation Substrate Model Inhibitor Model 1 Inhibitor Model 2
    • Efflux Ratio
    • ATPase Activation assay
    Güner-Henry Score 33 compounds inhibition of [3H]-digoxin transport in Caco-2) Chang, Bahadurri et al, DMD 34, 1976-1984 (2006) A B
  • 10. Summary of P-gp prospective screen
    • Evolution and application of earlier pharmacophores
      • Pharmacophores retrieve ~50% known P-gp substrates or inhibitors in hit lists from database searching
    • Discovered novel P-gp interacting classes
      • Potassium channel blocker (Repaglinide)
      • Retinoid (Acitretin)
      • Nonpeptide angiotensin II antagonist (Telmisartan)
      • Prostaglandin analogue (Misoprostal)
    • Expanded P-gp interaction profile
      • Penicillin based  -lactam antibiotic (Nafcillin)
      • Steroid hormone (cholecalciferol)
      •  2 -adrenoceptor agonist (Salmeterol)
    Chang, Bahadurri et al, DMD 34, 1976-1984 (2006)
  • 11.
    • 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
    Bigger datasets require other tools P-gp +ve P-gp -ve Balakin et al.,Curr Drug Disc Technol 2:99-113, 2005.
  • 12. Future …
      • Virtually every type of algorithm/ descriptor combination used on available P-gp data
      • Need for much larger public / available datasets –
      • These will likely be used with machine learning algorithms
      • Transporter databases in the future with on line models?
      • Limited accessibility of models
      • Applicability domain of models / confidence in predictions rarely evaluated
      • Use experience with P-gp to model other transporters, find substrates etc
    Chang, Bahadurri et al, DMD 34, 1976-1984 (2006)
  • 13. Open source tools for transporter 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
    $ $$$$$$
  • 14. More collaborations, integrating models into scientific social networks Drug Disc Today, 14: 261-270, 2009
  • 15. MRP BCRP P-gp Molecule Databases In vitro testing hPEPT Transporter Pharmacophores or other model types Feedback of new substrates or inhibitors More Transporters - More Models Ekins, in Ecker G and Chiba P, Transporters as drug carriers, John Wiley and Sons. P215-227, 2009. MRP BCRP P-gp Molecule Databases In vitro testing hPEPT Transporter Pharmacophores Feedback of new substrates or inhibitors
  • 16. 95% 5% >95% 5% Bile acid pool: 3-5g Circulate 6-10 daily Total turnover rate: 20-30g Lost in feces: < 0.5g Enterohepatic Circulation Expressed at high levels in the terminal ileum where it mediates bile acid recovery. studies have implicated secondary bile acids as important in the development of colorectal cancer. hASBT inhibition results in increased colonic exposure to cytotoxic secondary bile acids. an association between a polymorphism in the SLC10A2 gene and the risk of colorectal adenomatous polyps. The human Apical Sodium-dependent Bile Acid Transporter Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009
  • 17. Computational Models for ASBT
    • Common feature (HipHop) Pharmacophore Modeling
    • Identify common molecular features based on the superposition of active compounds
    • Arrange key features that are important for biological activity
    • Predicts the compound as “active” or “inactive” based on fit to the pharmacophore
    • Quantitative Pharmacophore Modeling
    • A quantitative pharmacophore was generated using the HypoGen method.
    • The statistical relevance of the pharmacophore was evaluated based on structure activity correlation coefficient and cost value relative to the null hypothesis.
    • Bayesian Model
    • Classification model uses fragment and simple interpretable descriptors
    • Aid in understanding features in actives and inactives
    • SCUT Database
    • Total 796 compounds (656 drugs in the SCUT 2008 database plus additional drug metabolites and drugs of abuse)
  • 18. Computational Models for ASBT - process 38 Bayesian models + validation with test sets Test set 1 N= 30 from same lab Test set 2 N = 19 from literature sources
  • 19. HipHop Pharmacophore Model
    • A common feature HipHop pharmacophore model of hASBT was generated from the 11 most potent inhibitors.
    • The most active compound mesoridazine was used to create a shape restriction.
    • retrieved 58 hits - 15 selected and assessed for ASBT inhibition. 8 were were potent inhibitors ( K i < 100 μ M).
    • The model consisted of:
      • 2 hydrogen bond acceptors (Green)
      • 2 hydrophobes (Blue)
    Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009
  • 20. Calcium Channel Blockers and HMG CoA-reductase Inhibitors
    • Most potent inhibitors were calcium channel blockers and HMG CoA-reductase inhibitors
    • Five from secondary screen (*)
    • Additional calcium channel blockers and statins were studied.
    • Dihydropyridines are more potent hASBT inhibitors than other classes of calcium channel blockers .
    Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009 Compound Drug Class K i Value ( µM) Nifedipine Dihydropyridine 3.87±0.64 Nisoldipine Dihydropyridine 4.77 ± 1.0 5 Nimodipine* Dihydropyridine 5.75±0.72 Simvastatin* Statin 10.4± 2.1 Fluvastatin* Statin 11.5±0.8 Isradipine Dihydropyridine 19.4±3.0 Lovastatin* Statin 21.6±2.3 Nemadipine Dihydropyridine 23.1±4.1 Nicardipine Dihydropyridine 32 . 4 ± 3 . 1 Nitrendipine Dihydropyridine 34.1± 5.1 Amlodipine* Dihydropyridine 42.1±7.7 Felodipine Dihydropyridine 49.7 ± 7.0 Diltiazem   Benzothiazepines 211 ± 21 Verapamil Phenylalkylamine 26 6 ± 2 2
  • 21. Quantitative Pharmacophore Model hASBT
    • A quantitative pharmacophore was generated with 38 compounds using the HypoGen method
    • The model consisted of:
      • 1 hydrogen bond acceptors (Green)
      • 3 hydrophobes (Blue)
      • 5 excluded volumes
    • r : 0.815.
    Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009
  • 22. Molecular function class fingerprints of maximum diameter 6 (FCFP_6), + simple interpretable descriptors leaving 20% out 100 times, ROC was 0.78; concordance 72.5%; specificity 81.0%; Sensitivity 58.1%. Bayesian and pharmacophore perform similarly > 80% correct with n= 30 test set All models perform poorly with literature test set Bayesian Model hASBT +ve -ve Dihydropyridine substructure Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009
  • 23. ASBT Conclusions
    • A large number of drugs from diverse classes were found to be hASBT inhibitors.
    • Most potent inhibitors were dihydropyridine calcium channel blockers, HMG CoA-reductase inhibitors, or diuretics.
    • A HipHop pharmacophore and a quantitative pharmacophore of hASBT was generated and have features in common.
    • Literature test set – poor predictions with both models – variability in data
    • Future pharmacoepidemiologic studies will attempt to elucidate a possible relationship between hASBT inhibition and the incidence of colon cancer.
    • Current data conflicting on role.
    • Novel insights from in vitro and in silico methods for ASBT interactions
    Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009
  • 24. hOCTN2
    • 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)
  • 25. Possible Association between Clinical Rhabdomyolysis and hOCTN2 Inhibition Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
  • 26. +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine
  • 27. 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
  • 28. 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 Diao et al., Mol Pharm, 7: 2120-2131, 2010
  • 29. Proactive database searching - Prioritize compounds for testing in vitro Understand drug interactions In silico allows rapid parallel optimization vs transporters or other properties See poster W3001 B. Astorga et al. Provide novel insights into the molecular interaction of inhibitors Repurpose - reposition FDA drugs Summing up
  • 30. 2D Similarity search with “hit” from transporter screening Export database and use for 3D searching with a pharmacophore or other model for transporter Suggest approved drugs for testing - may also indicate other uses if it is present in more than one database Suggest in silico hits for in vitro screening Key databases of structures and bioactivity data FDA drugs database Could future transporter models help Repurpose FDA drugs
  • 31. Acknowledgments
    • University of Maryland
      • Xiaowan Zheng
      • Lei Diao
      • Cheng Chang
      • Praveen Bahadduri
      • Peter W. Swaan
      • James E. Polli
    • Pfizer
      • Rishi Gupta
      • Eric Gifford
      • Ted Liston
      • Chris Waller
    • Konstantin Balakin (Orchemed)
    • Antony J. Williams (RSC)
    • Accelrys
    • CDD
    • [email_address]