In silico approaches to predict drug-transporter interaction profiles: Data mining, model generation, and link to cholestasis

Sankalp Jain, Gerhard F. Ecker

    Publications: Contribution to bookChapterPeer Reviewed

    Abstract

    Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as cholestasis, which comprises a major challenge in drug development process. Thus, computer-based (in silico) models that can predict the pharmacological and toxicological profiles of these small molecules with respect to liver transporters may help in the early prioritization of compounds and hence may lower the high attrition rates. In this chapter, we provide a protocol for in silico prediction of cholestasis by generating validated predictive models. In addition to the two-dimensional molecular descriptors, we include transporter inhibition predictions as descriptors and evaluate the influence of the same on the performance of the cholestasis models.

    Original languageEnglish
    Title of host publicationMethods in Molecular Biology
    PublisherHumana Press, Inc.
    Pages383-396
    Number of pages14
    DOIs
    Publication statusPublished - 1 Jan 2019

    Publication series

    SeriesMethods in Molecular Biology
    Volume1981
    ISSN1064-3745

    Austrian Fields of Science 2012

    • 301207 Pharmaceutical chemistry

    Keywords

    • Applicability domain
    • Cholestasis
    • Classification model
    • Data curation
    • Drug-induced liver injury
    • In silico toxicology
    • Liver transporters
    • Machine learning
    • QSAR
    • Transporter prediction

    Cite this