This document summarizes Sean Ekins' presentation on applying computational models to predict toxicity related to drug transporters. It discusses developing pharmacophore models and Bayesian machine learning approaches for various transporters like OCTs, MATE1, MRP4, NTCP, and hOCTN2 based on literature data. Validation of the models with in vitro testing showed good prediction of inhibitors. The models were also used to search drug databases to find new inhibitors and substrates of the transporters. Limitations and future work applying these techniques to other transporters and making the models openly available are discussed.

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

5. 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

6. 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)

7. 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)

8. 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

9. 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)

10. 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)

11. 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)

12. 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)

13. 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)

14. 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)

15. 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)

16. 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)

17. 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)

18. 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)

19. 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?

20. 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 @...

21. 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)

22.  Email: ekinssean@yahoo.com
 Slideshare: http://www.slideshare.net/ekinssean
 Twitter: collabchem
 Blog: http://www.collabchem.com/
 Website: http://www.collaborations.com/CHEMISTRY.HTM