Mobile apps have been developed for drug discovery tasks like molecule sketching. This document proposes mobile apps could also predict drug-drug interactions mediated by drug transporters. Transporter models were built using published datasets in commercial and open source tools, and loaded into a mobile app. The models accurately predicted inhibition of a drug transporter. Such mobile apps could help prioritize in vivo drug-drug interaction testing by allowing transporter profiling of compounds on smartphones.

1. Mobile Apps for Drug-Drug Interaction Prediction – A Tool of the Future, now
Sean Ekins1, 2*, Alex M. Clark3, James E. Polli2 and Stephen H. Wright4
1Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
2Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201.
3 Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1
4Department of Physiology, University of Arizona, Tucson, Arizona, AZ 85724, U.S.A. *Adjunct Positions
Over the past decade we have seen increased numbers of studies publishing ligand-based and
protein-based models for drug transporters. The predominantly ligand-based methods generally use
small experimental datasets but can provide some new insights into structure activity relationships. In
addition such models have helped to identify new compounds as substrates or inhibitors of
transporters of interest. We have recently proposed that many of the transporters are promiscuous
and may require profiling of new chemical entities against multiple substrates for a specific
transporter. If one considers the number of transporters which have been experimentally
characterized, one would require access to tens if not hundreds of models, in order to profile a
potential new drug thoroughly for drug-drug interactions. Further, it should be noted that virtually all of
the published ligand-based transporter models are only accessible to those involved in creating them
and rarely shared. We are currently seeing the development of mobile devices and apps that can be
applied for scientific uses. A number of mobile apps have been created for sketching and sharing
molecules (e.g. MMDS, MolPrime and ODDT). More recently we have created mobile apps that
combine Bayesian models and open source fingerprint descriptors to enable models that can be
used within a mobile app without connecting to the internet (TB Mobile, MMDS, Approved Drugs,
MolPrime). We now propose how such mobile apps could be used to predict the ligand interactions
with transporters. We have used recently published transporter datasets (e.g. MATE, OCT’s, OCTN2
and NTCP) to build preliminary models in several commercial tools and in mobile apps, and compare
the statistics. In addition, several transporter datasets extracted from ChEMBL were used with a
novel model building approach. Our ultimate goal is to make such models available in a dedicated
transporter DDI profiling app that would complement a desktop web-based application. Predicting
DDI for various transporters using computational models may soon be within reach of anyone with a
smart phone. Such tools could help prioritize which substrates should be used for in vivo DDI testing
and sharing of such models is inherently feasible.
Abstract
Introduction
Methods
Results
References
Conclusions
1. Clark AM, Sarker M, Ekins S (2014) J Cheminform 6: 38.
2. Ekins S, Clark AM, Sarker M (2013) J Cheminform 5: 13.
3. Ekins S, Clark AM, Williams AJ (2012) Mol Informat 31: 585-597.
4. Williams AJ, Ekins S, Clark AM, Jack JJ, Apodaca RL (2011) Drug DiscovToday 16: 928-939.
5. Ekins S, Clark AM, Williams AJ (2013) ACS Sustain Chem Eng 1: 8-13.
6. Clark AM, Williams AJ, Ekins S (2013) Chem-Bio Informatics J 13: 1-18.
7. Astorga B, Ekins S, Morales M, Wright SH (2012) J Pharmacol Exp Ther 341: 743-755.
8. Diao L, Ekins S, Polli JE (2010) Mol Pharm 7: 2120-2130.
9. Diao L, Ekins S, Polli JE (2009) Pharm Res 26: 1890-1900.
10. Zheng X, Ekins S, Raufman JP, Polli JE (2009) Mol Pharm 6: 1591-1603.
11. Dong Z, Ekins S, Polli JE (2013) Mol Pharm 10: 1008-1019.
12. Wittwer MB, Zur AA, Khuri N, Kido Y, Kosaka A, et al. (2013) J Med Chem 56: 781-795.
13. Kido Y, Matsson P, Giacomini KM (2011) J Med Chem 54: 4548-4558.
14. Clark A, Sarker M, Ekins S (2014) J Cheminform 6: 38.
15. Clark AM, Ekins S (2015) Submitted.
16. Clark AM, Dole K, Coulon-Spector A, McNutt A, Grass G, et al. (2015) Submitted.
17. Imamura Y, Murayama N, Okudaira N, Kurihara A, Inoue K, et al. (2013) Pharm Res 30: 447-457.
Table 1. 5-fold and Leave one out (LOO) validation with Bayesian models generated with
Discovery Studio and Open Models implemented in a mobile app MMDS.
Figure 1. A. Models in MMDS mobile app B. Model details C. Selecting models D. Predictions for
DX-619 in MMDS
A
B
C
D
Figure 2. A. Selected examples of extracted datasets from ChEMBL and the analysis leading to the
detection of a suitable activity threshold. (data and models available at http://molsync.com/bayesian2)
In response to the shift towards mobile computing, we have developed mobile apps for drug
discovery, leveraging years of research in cheminformatics [1-6]. An app that enables a scientist to
select a molecule and score it with models for various transporters of relevance for drug-drug
interactions is needed. We have focused on transporters because of our prior modeling efforts [7-11].
Previously we have published several transporter models with Discovery Studio [7-11]. We have now
analyzed several larger published datasets from other groups for MATE1 [12] and OCT2 [13] which
we have used to generate Bayesian models. Recently we described the development of open
source FCFP6 and ECFP6 descriptors [14] and the Bayesian algorithm [15,16] which enables us to
build models with open source tools. To illustrate the utility of transporter models built with open
descriptors and algorithms, 5-fold cross validation and leave one out (LOO) was used. Cutoffs for
actives / inactives were as previously described.
Transporter models were developed and loaded into MMDS (http://molmatinf.com/) (Fig 1) and then
used to score DX-619, a MATE inhibitor [17] (Fig 1D) . Several transporter datasets from ChEMBL
(Fig 2) are used to illustrate our work to determine a cutoff for activity [15]. These models are also
accessible as part of a set of over 2000 models at http://molsync.com/bayesian2.
• 5-fold and LOO ROC values similar with commercial and open models / descriptors (Table 1).
• There is now the possibility to build models for different substrate probes for a single transporter.
• As an example DX-619 was predicted with one MATE1 Bayesian model for inhibition of MPP
transport as a likely inhibitor and scored lower for inhibition of ASP(+) transport (Fig 1D).
• A similar approach could be taken for drug metabolizing enzymes, receptors etc. ChEMBL has
1000’s of datasets, including transporters (Fig 2).
• Analysis of a molecule to predict propensity for drug-drug interactions mediated via transporters
could be useful to suggest future experiments.
• As the models can be stored in the mobile app and would not have to communicate over the web,
such calculations would be secure.
• Accessible transporter data and models is the future and it is here, now.
Model (N active/ total) Reference Discovery Studio* FCFP6
+ 8 ADME descriptors
Discovery Studio
ECFP6 only
Open Model
ECFP6 only
MATE1 (81/841) [12] 0.65 (LOO 0.72) 0.62 (LOO 0.68) 0.68 (LOO 0.67)
MATE 1 (24/45) [7] 0.75 (LOO 0.79) 0.63 (LOO 0.72) 0.70 (LOO 0.74)
MATE 2K (21/45) [7] 0.59 (LOO 0.72) 0.60 (LOO 0.81) 0.75 (LOO 0.73)
OCT2 (243/904) [13] 0.80 (LOO 0.81) 0.76 (LOO 0.79) 0.78 (LOO 0.79)
OCTN2 (30/49) [8,9] 0.85 (LOO 0.89) 0.71 (LOO 0.77) 0.85 (LOO 0.88)
ASBT (17/38) [10] 0.75 (LOO 0.91) 0.73 (LOO 0.87) 0.74 (LOO 0.76)
NTCP (50/107) [11] 0.61 (LOO 0.73) 0.56 (LOO 0.68) 0.49 (LOO 0.53)
*SE acknowledges Biovia for providing Discovery Studio.