Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data.
Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI.
U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States.
Xiao
X
Zhu
U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States. Electronic address: Naomi.Kruhlak@fda.hhs.gov.
Naomi L
NL
Kruhlak
D000465:Algorithms; D000667:Ampicillin / Q000633:toxicity; D000818:Animals; D000900:Anti-Bacterial Agents / Q000633:toxicity; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000633:toxicity; D001205:Ascorbic Acid / Q000633:toxicity; D001649:Bile Duct Diseases / Q000139:chemically induced / Q000473:pathology; D002138:Calibration; D056486:Chemical and Drug Induced Liver Injury / Q000145:classification / Q000201:enzymology / Q000473:pathology*; D002779:Cholestasis / Q000139:chemically induced / Q000473:pathology; D057225:Data Mining; D016208:Databases, Factual; D004348:Drug Labeling; D023381:Endpoint Determination; D006801:Humans; D064704:Levofloxacin / Q000633:toxicity; D008099:Liver / Q000201:enzymology / Q000473:pathology; D009288:Naproxen / Q000633:toxicity; D011358:Product Surveillance, Postmarketing; D021281:Quantitative Structure-Activity Relationship; D018675:Toxicity Tests / Q000706:statistics & numerical data*; D014815:Vitamins / Q000633:toxicity
D000900:Anti-Bacterial Agents; D000894:Anti-Inflammatory Agents, Non-Steroidal; D014815:Vitamins; D009288:Naproxen; D064704:Levofloxacin; D000667:Ampicillin; D001205:Ascorbic Acid
Drug-induced liver injury; Post-market safety; Predictive toxicology; QSAR