• Decrease font size
  • Return font size to normal
  • Increase font size
U.S. Department of Health and Human Services

Scientific Publications by FDA Staff

  • Print
  • Share
  • E-mail

Search Publications



Starting Date

Ending Date

Order by

Entry Details

Toxicol Sci 2013 Nov;136(1):242-9

Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs.

Chen M, Hong H, Fang H, Kelly R, Zhou G, Borlak J, Tong W


Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry, but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Since the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the FDA-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of two additional independent validation sets, and the three validation datasets have a total of 483 unique drugs. We observed that the QSAR model's performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus the model's applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compounds risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

Category: Journal Article
PubMed ID: #23997115 DOI: 10.1093/toxsci/kft189
Includes FDA Authors from Scientific Area(s): Toxicological Research
Entry Created: 2013-09-03 Entry Last Modified: 2014-01-03