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Rapid CommunicationShort Communication

Making Transporter Models for Drug–Drug Interaction Prediction Mobile

Sean Ekins, Alex M. Clark and Stephen H. Wright
Drug Metabolism and Disposition October 2015, 43 (10) 1642-1645; DOI: https://doi.org/10.1124/dmd.115.064956
Sean Ekins
Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
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Alex M. Clark
Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
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Stephen H. Wright
Collaborations Pharmaceuticals, Inc., and Collaborations in Chemistry, Fuquay-Varina, North Carolina (S.E.); Collaborative Drug Discovery, Burlingame, California (S.E.); Molecular Materials Informatics, Inc., Montreal, Quebec, Canada (A.M.C.); and Department of Physiology, University of Arizona, Tucson, Arizona (S.H.W.)
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Abstract

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure–activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug–drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug–drug interaction testing and enable open sharing of models.

Footnotes

    • Received April 16, 2015.
    • Accepted July 21, 2015.
  • The open models and descriptors research was supported by the National Institutes of Health National Center for Advancing Translational Sciences [Grant 9R44TR000942-02]. Mobile app development for transporters was supported by the National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases [Grants 5R01DK058251-14 and 1R01DK080801-5]. All Discovery Studio models are available from the authors upon request. Open models are accessible at http://molsync.com/transporters. ChEMBL models are accessible at http://molsync.com/bayesian2.

  • dx.doi.org/10.1124/dmd.115.064956.

  • ↵Embedded ImageThis article has supplemental material available at dmd.aspetjournals.org.

  • Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 43 (10)
Drug Metabolism and Disposition
Vol. 43, Issue 10
1 Oct 2015
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Rapid CommunicationShort Communication

Mobile Transporter Models

Sean Ekins, Alex M. Clark and Stephen H. Wright
Drug Metabolism and Disposition October 1, 2015, 43 (10) 1642-1645; DOI: https://doi.org/10.1124/dmd.115.064956

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Rapid CommunicationShort Communication

Mobile Transporter Models

Sean Ekins, Alex M. Clark and Stephen H. Wright
Drug Metabolism and Disposition October 1, 2015, 43 (10) 1642-1645; DOI: https://doi.org/10.1124/dmd.115.064956
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