Identification of structure-activity relationships for adverse effects of  pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of  drug-induced hepatobiliary and urinary tract toxicities.
This report describes the development of quantitative structure-activity relationship (QSAR) models for predicting rare drug-induced liver and urinary tract injury in humans based upon a database of post-marketing adverse effects (AEs) linked to approximately 1600 chemical structures. The models are based upon estimated population exposure using AE proportional reporting ratios. Models were constructed for 5 types of liver injury (liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, gall bladder disorders) and 6 types of urinary tract injury (acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, urolithiases). Identical training data sets were configured for 4 QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Predictive Data Miner). Model performance was optimized and was shown to be affected by the AE scoring method and the ratio of the number of active to inactive drugs. The best QSAR models exhibited an overall average 92.4% coverage, 86.5% specificity and 39.3% sensitivity. The 4 QSAR programs were demonstrated to be complementary and enhanced performance was obtained by combining predictions from 2 programs (average 78.4% specificity, 56.2% sensitivity). Consensus predictions resulted in better performance as judged by both internal and external validation experiments.
Matthews
Edwin J
EJ
US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff (ICSAS), Silver Spring, MD 20993, USA. edwin.matthews@fda.hhs.gov
Ursem
Carling J
CJ
Kruhlak
Naomi L
NL
Benz
R Daniel
RD
Sabaté
David Aragonés
DA
Yang
Chihae
C
Klopman
Gilles
G
Contrera
Joseph F
JF
D016907:Adverse Drug Reaction Reporting Systems; D001660:Biliary Tract Diseases / Q000139:chemically induced / Q000175:diagnosis*; D056486:Chemical and Drug Induced Liver Injury / Q000175:diagnosis* / Q000209:etiology; D016000:Cluster Analysis; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D042241:Early Diagnosis; D023381:Endpoint Determination; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000737:chemistry*; D011358:Product Surveillance, Postmarketing; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D014481:United States; D014486:United States Food and Drug Administration; D014570:Urologic Diseases / Q000139:chemically induced / Q000175:diagnosis*
D004364:Pharmaceutical Preparations