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Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
Applying computational models for transporters to predict toxicity
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Applying computational models for transporters to predict toxicity

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  • 1. Applying Computational Models for Transporters to Predict Toxicity Sean Ekins Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC27526, USA.
  • 2. Clinical importance of transporters • Increased attention on transporter inhibition • Drug-drug interactions • Effects of polymorphisms in transporters • Many new potential drug targets • in vitro models may be limited in throughput • in vivo more complicated - multiple transporters with overlapping substrate specificities. • in silico – in vitro approach has value in targeting testing of compounds with a high probability of activity.
  • 3. Nature Reviews Drug Discovery 9, 215–236 (1 March 2010) Transporters in this presentation
  • 4. Ideal when we have few molecules for training In silico database searching Accelrys (Biovia) 3D QSAR pharmacophore or common feature pharmacophore 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 P-gp OATPs OCT1 OCT2 BCRP hOCTN2 ASBT hPEPT1 hPEPT2 NTCP MATE1 MRP4
  • 5. 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 Used with simple descriptors and FCFP_6 fingerprints Bayesian approach used widely with other ADME/Tox datasets PAPER ID: 22183 “Progress in computational toxicology” (final paper number: 125)
  • 6. 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)
  • 7. 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 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%) 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. vinblastine
  • 8. Substrate Affinity for hOCTN2 Km (uM) Principal Acetyl-L-Carnitine 8.5 2 Ipratropium 53 1 Ketoprofen-Glycine-L-Carnitine 58.5 1 Ketoprofen-L-Carnitine 77 1 L-Carnitine 5.3 2 Mildronate 26 1 Naproxen-L-Carnitine 257 0 Valproyl-Glycolic Acid-L- Carnitine 161 0 Valproyl L-Carnitine 132 0 hOCTN2 Substrate pharmacophore Overlap of substrate and inhibitor pharmacophores Training set from various literature sources L-carnitine mapped to substrate pharmacophore Green = HBA Blue = hydrophobic Red = +ve ionizable Grey = exclude volume Pharmacophore used to search drugs database – 16/30 compounds associated with rhabdomyolysis Ekins et al., Mol Pharmaceutics 9:905-913 (2012)
  • 9. MATE1 • Multidrug and toxin extruder – organic cations • Little work on SAR • Combined in vitro with pharmacophore and Bayesian models • Weak correlation with LogP for hMATE1 • 26 molecule common feature and quantitative models for hMATE1 • Multiple iterations Astorga et al., JPET 341: 743-755 (2012)
  • 10. Common feature hMATE1 pharmacophore Quantitative hMATE1 pharmacophores N= 24 N=43 N=46 Green = HBA Blue = hydrophobic Red = +ve ionizable Purple = HBD Astorga et al., JPET 341: 743-755 (2012)
  • 11. hMATE1 Bayesian Model Features • Features +ve -ve ROC = 0.88, leave out 50% x 100 ROC = 0.82 Bad features pyrole -low basicity Charge important for increasing interaction with transporter Astorga et al., JPET 341: 743-755 (2012)
  • 12. Pharmacophores with different substrate probes • Used the 6 compounds from Kido et al., 2011 • Compared with N46 model • Different features – possible different binding sites • Probe dependent in vitro effects analogous to P4503A4 Astorga et al., JPET 341: 743-755 (2012)
  • 13. MRP4 • Multidrug resitance protein 4 (MRP4) • Expressed widely • Transports protease inhibitors (HIV treatments HAART) and anticancer drugs • Increase in cancer (Hodgkin’s lymphoma, lung, testicular etc) in these patients requires HIV and anticancer drugs • Potential for interactions –inhibitors increase toxicity of substrates Fukuda et al., Mol Pharmacol 84: 361-371 (2013)
  • 14. MRP4 Pharmacophore • Nelfinavir> Ritonavir> amprenavir, indinavir, saquinavir Common feature pharmacophore • Literature dataset of 10 MRP inhibitors (Russel et al., Trends Pharm Sci 29: 200-207, 2008) common feature and quantitative models • Searched drug dataset and retrieved 9 known MRP4 substrates • PGE2 (red) shared most features, quercetin (grey) poor match to features • Nelfinavir enhances cytotox of methotrexate Green = HBA Blue = hydrophobic Purple = HBD Fukuda et al., Mol Pharmacol 84: 361-371 (2013)
  • 15. NTCP • Human Sodium taurocholate cotransporting polypeptide (NTCP) • Bile acid transporter – basolateral membrane of hepatocytes • Also transports drugs (rosuvastatins) • Potential for clinically relevant drug- drug interactions – Micafungin and Cyclosprin A (Clin Pharmacol 45: 954 (2005) • Goal – find additional FDA drugs and develop models Dong et al., Mol Pharmaceutics 10: 1008-1019 (2013)
  • 16. NTCP Common feature Pharmacophore • 11 inhibitors and 12 inactives • Screened FDA drugs (ezetimibe shape feature) • Test more compounds • Develop Bayesian model (N = 50) • Identified 27 novel inhibitors including Angiotensin II antagonists SAR in series from 12 -3000uM Dong et al., Mol Pharmaceutics 10: 1008-1019 (2013)
  • 17. NTCP Bayesian Model +ve -ve Using 8 simple descriptors and FCFP_6 fingerprints ROC = 0.77, leave out testing ROC declined as group size increased Model able to predict 7/10 High scoring molecules in test set and 7/12 low scoring Dong et al., Mol Pharmaceutics 10: 1008-1019 (2013)
  • 18. Summary • Proactive database searching - Prioritize compounds for testing in vitro • Provide novel insights into the molecular interaction of inhibitors • Repurpose - reposition FDA drugs • NTCP – recent work – quantitative pharmacophore + testing • NTCP – substrate model • Predominant - inhibitor data • Open to using models for prospective testing of new molecules • Potential to apply the same technique with other transporters • Parallel profiling • Make models available on website / mobile app?
  • 19. PAPER ID: 22104 “Collaborative sharing of molecules and data in the mobile age” (final paper number: 43) DIVISION: COMP; DAY & TIME OF PRESENTATION: August 10, 2014 from 4:45 pm to 5:15 pm LOCATION: Moscone Center, West Bldg., Room: 2005 PAPER ID: 22094 “Expanding the metabolite mimic approach to identify hits for Mycobacterium tuberculosis ” (final paper number: 78) DIVISION: COMP: DAY & TIME OF PRESENTATION: August 11, 2014 from 9:00 am to 9:30 am LOCATION: Moscone Center, West Bldg., Room: 2005 PAPER ID: 22120 “Why there needs to be open data for ultrarare and rare disease drug discovery” (final paper number: 48) DIVISION: CINF:SESSION DAY & TIME OF PRESENTATION: August 11, 2014 from 10:50 am to 11:20 am LOCATION: Palace Hotel, Room: Marina PAPER ID: 22183 “Progress in computational toxicology” (final paper number: 125) DIVISION: TOXI: DAY & TIME OF PRESENTATION: August 12, 2014 from 6:30 pm to 10:30 pm LOCATION: Moscone Center, North Bldg. , Room: 134 PAPER ID: 22091 “Examples of how to inspire the next generation to pursue computational chemistry/cheminformatics” (final paper number: 100) DIVISION: CINF: Division of Chemical Information DAY & TIME OF PRESENTATION: August 13, 2014 from 8:25 am to 8:50 am LOCATION: Palace Hotel, Room: Presidio PAPER ID: 22176 “Applying computational models for transporters to predict toxicity” (final paper number: 132) DIVISION: TOXI: DAY & TIME OF PRESENTATION: August 13, 2014 from 9:45 am to 10:05 am LOCATION: InterContinental San Francisco, Room: Grand Ballroom A PAPER ID: 22186 “New target prediction and visualization tools incorporating open source molecular fingerprints for TB mobile version 2” (final paper number: 123) DIVISION: CINF: DAY & TIME OF PRESENTATION: August 13, 2014 from 1:35 pm to 2:05 pm LOCATION: Palace Hotel, Room: California Parlor You can find me @...
  • 20. Collaborators  James E. Polli (University of Maryland)  Zhongqi Dong  Lei Diao  John D. Schuetz and Lab (St Jude Childrens research Hospital)  Stephen H. Wright and Lab (University of Arizona)  Bethzaida Astorga  Peter Swaan (University of Maryland)
  • 21.  Email: ekinssean@yahoo.com  Slideshare: http://www.slideshare.net/ekinssean  Twitter: collabchem  Blog: http://www.collabchem.com/  Website: http://www.collaborations.com/CHEMISTRY.HTM

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