Skip to main content
Advertisement

Main menu

  • Home
  • Articles
    • Current Issue
    • Fast Forward
    • Latest Articles
    • Special Sections
    • Archive
  • Information
    • Instructions to Authors
    • Submit a Manuscript
    • FAQs
    • For Subscribers
    • Terms & Conditions of Use
    • Permissions
  • Editorial Board
  • Alerts
    • Alerts
    • RSS Feeds
  • Virtual Issues
  • Feedback
  • Submit
  • Other Publications
    • Drug Metabolism and Disposition
    • Journal of Pharmacology and Experimental Therapeutics
    • Molecular Pharmacology
    • Pharmacological Reviews
    • Pharmacology Research & Perspectives
    • ASPET

User menu

  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Drug Metabolism & Disposition
  • Other Publications
    • Drug Metabolism and Disposition
    • Journal of Pharmacology and Experimental Therapeutics
    • Molecular Pharmacology
    • Pharmacological Reviews
    • Pharmacology Research & Perspectives
    • ASPET
  • My alerts
  • Log in
  • My Cart
Drug Metabolism & Disposition

Advanced Search

  • Home
  • Articles
    • Current Issue
    • Fast Forward
    • Latest Articles
    • Special Sections
    • Archive
  • Information
    • Instructions to Authors
    • Submit a Manuscript
    • FAQs
    • For Subscribers
    • Terms & Conditions of Use
    • Permissions
  • Editorial Board
  • Alerts
    • Alerts
    • RSS Feeds
  • Virtual Issues
  • Feedback
  • Submit
  • Visit dmd on Facebook
  • Follow dmd on Twitter
  • Follow ASPET on LinkedIn
Research ArticleArticle

A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury

Sean Ekins, Antony J. Williams and Jinghai J. Xu
Drug Metabolism and Disposition December 2010, 38 (12) 2302-2308; DOI: https://doi.org/10.1124/dmd.110.035113
Sean Ekins
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antony J. Williams
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jinghai J. Xu
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF + SI
  • PDF
Loading

Abstract

Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

Footnotes

  • S.E. consults for various pharmaceutical and software companies including Merck, although he did not receive any payment for this study. J.J.X. is currently employed by Merck, was previously employed by Pfizer, and has stock ownership in both companies as well as in other biopharmaceutical companies.

  • The structures of all compounds in the test and training sets as well as the set of recently approved drugs are available in sdf format online, and the Bayesian model protocols used in Discovery Studio are available from the authors upon request.

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

    doi:10.1124/dmd.110.035113.

  • ↵Embedded Image The online version of this article (available at http://dmd.aspetjournals.org) contains supplemental material.

  • ABBREVIATIONS:

    DILI
    drug-induced liver injury
    HIAT
    human hepatocyte imaging assay technology
    ECFC_6
    extended connectivity functional class fingerprints of maximum diameter 6
    SMARTS
    Smiles Arbitrary Target Specification
    FCFP
    functional class fingerprint
    ROC
    receiver operator characteristic
    PCA
    principal component analysis
    AUC
    area under the curve.

  • Received June 22, 2010.
  • Accepted September 15, 2010.
  • Copyright © 2010 by The American Society for Pharmacology and Experimental Therapeutics
View Full Text

 

DMD articles become freely available 12 months after publication, and remain freely available for 5 years. 

Non-open access articles that fall outside this five year window are available only to institutional subscribers and current ASPET members, or through the article purchase feature at the bottom of the page. 

 

  • Click here for information on institutional subscriptions.
  • Click here for information on individual ASPET membership.

 

Log in using your username and password

Forgot your user name or password?

Purchase access

You may purchase access to this article. This will require you to create an account if you don't already have one.
PreviousNext
Back to top

In this issue

Drug Metabolism and Disposition: 38 (12)
Drug Metabolism and Disposition
Vol. 38, Issue 12
1 Dec 2010
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Back Matter (PDF)
  • Editorial Board (PDF)
  • Front Matter (PDF)
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Drug Metabolism & Disposition article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury
(Your Name) has forwarded a page to you from Drug Metabolism & Disposition
(Your Name) thought you would be interested in this article in Drug Metabolism & Disposition.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Research ArticleArticle

A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury

Sean Ekins, Antony J. Williams and Jinghai J. Xu
Drug Metabolism and Disposition December 1, 2010, 38 (12) 2302-2308; DOI: https://doi.org/10.1124/dmd.110.035113

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
Research ArticleArticle

A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury

Sean Ekins, Antony J. Williams and Jinghai J. Xu
Drug Metabolism and Disposition December 1, 2010, 38 (12) 2302-2308; DOI: https://doi.org/10.1124/dmd.110.035113
Reddit logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Acknowledgments.
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF + SI
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • Mass Balance Recovery and Disposition of AZD4831 in Humans
  • Biotransformation of AZD4831 in Animals and Humans
  • AKRs and GUSs in Testosterone Disposition
Show more Articles

Similar Articles

Advertisement
  • Home
  • Alerts
Facebook   Twitter   LinkedIn   RSS

Navigate

  • Current Issue
  • Fast Forward by date
  • Fast Forward by section
  • Latest Articles
  • Archive
  • Search for Articles
  • Feedback
  • ASPET

More Information

  • About DMD
  • Editorial Board
  • Instructions to Authors
  • Submit a Manuscript
  • Customized Alerts
  • RSS Feeds
  • Subscriptions
  • Permissions
  • Terms & Conditions of Use

ASPET's Other Journals

  • Journal of Pharmacology and Experimental Therapeutics
  • Molecular Pharmacology
  • Pharmacological Reviews
  • Pharmacology Research & Perspectives
ISSN 1521-009X (Online)

Copyright © 2023 by the American Society for Pharmacology and Experimental Therapeutics