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Research ArticleArticle

Toward Predicting Drug-Induced Liver Injury: Parallel Computational Approaches to Identify Multidrug Resistance Protein 4 and Bile Salt Export Pump Inhibitors

Matthew A. Welch, Kathleen Köck, Thomas J. Urban, Kim L. R. Brouwer and Peter W. Swaan
Drug Metabolism and Disposition May 2015, 43 (5) 725-734; DOI: https://doi.org/10.1124/dmd.114.062539
Matthew A. Welch
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland (M.A.W., P.W.S.); Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.K., T.J.U., K.L.R.B.); Center for Human Genome Variation, Duke University Medical Center, Durham, North Carolina (T.J.U.)
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Kathleen Köck
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland (M.A.W., P.W.S.); Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.K., T.J.U., K.L.R.B.); Center for Human Genome Variation, Duke University Medical Center, Durham, North Carolina (T.J.U.)
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Thomas J. Urban
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland (M.A.W., P.W.S.); Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.K., T.J.U., K.L.R.B.); Center for Human Genome Variation, Duke University Medical Center, Durham, North Carolina (T.J.U.)
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Kim L. R. Brouwer
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland (M.A.W., P.W.S.); Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.K., T.J.U., K.L.R.B.); Center for Human Genome Variation, Duke University Medical Center, Durham, North Carolina (T.J.U.)
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Peter W. Swaan
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland (M.A.W., P.W.S.); Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.K., T.J.U., K.L.R.B.); Center for Human Genome Variation, Duke University Medical Center, Durham, North Carolina (T.J.U.)
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Abstract

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.

Footnotes

    • Received February 26, 2015.
    • Accepted February 27, 2015.
  • This work was supported by the National Institutes of Health National Institute of General Medical Sciences and National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases [Grants R01 GM041935, R01 DK61425, U01DK065201, and a National Institute of General Medical Sciences Collaborative Science Supplement]; and by Deutsche Forschungsgemeinschaft [Grant Ko4186/1-1]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

  • dx.doi.org/10.1124/dmd.114.062539.

  • ↵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 (5)
Drug Metabolism and Disposition
Vol. 43, Issue 5
1 May 2015
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Research ArticleArticle

Computational Modeling of MRP4 and BSEP to Predict DILI

Matthew A. Welch, Kathleen Köck, Thomas J. Urban, Kim L. R. Brouwer and Peter W. Swaan
Drug Metabolism and Disposition May 1, 2015, 43 (5) 725-734; DOI: https://doi.org/10.1124/dmd.114.062539

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Research ArticleArticle

Computational Modeling of MRP4 and BSEP to Predict DILI

Matthew A. Welch, Kathleen Köck, Thomas J. Urban, Kim L. R. Brouwer and Peter W. Swaan
Drug Metabolism and Disposition May 1, 2015, 43 (5) 725-734; DOI: https://doi.org/10.1124/dmd.114.062539
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