title: 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.
abstract: 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. Because 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 Food and Drug Administration-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 2 additional independent validation sets, and the 3 validation data sets 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 compound's risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.
authors.affiliation: * Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079;
authors.forename: Minjun
authors.initials: M
authors.lastname: Chen
authors.forename: Huixiao
authors.initials: H
authors.lastname: Hong
authors.forename: Hong
authors.initials: H
authors.lastname: Fang
authors.forename: Reagan
authors.initials: R
authors.lastname: Kelly
authors.forename: Guangxu
authors.initials: G
authors.lastname: Zhou
authors.forename: Jürgen
authors.initials: J
authors.lastname: Borlak
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
mesh_terms: D056486:Chemical and Drug Induced Liver Injury / Q000209:etiology*; D003198:Computer Simulation; D017277:Drug Approval; D004348:Drug Labeling; D064420:Drug-Related Side Effects and Adverse Reactions / Q000209:etiology*; D006801:Humans; D008958:Models, Molecular; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D004364:Pharmaceutical Preparations
keywords: drug label; drug-induced liver injury; external validation.; predictive model; quantitative structure-activity relationship; therapeutic categories