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.

[1]  Naomi L Kruhlak,et al.  A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database. , 2007, Regulatory toxicology and pharmacology : RTP.

[2]  R Daniel Benz,et al.  Toxicological and clinical computational analysis and the US FDA/CDER , 2007, Expert opinion on drug metabolism & toxicology.

[3]  N. Kruhlak,et al.  An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. , 2006, Regulatory toxicology and pharmacology : RTP.

[4]  Naomi L Kruhlak,et al.  Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling. , 2004, Current drug discovery technologies.

[5]  Naomi L Kruhlak,et al.  Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents , 2008, Toxicology mechanisms and methods.

[6]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[7]  Acute liver failure in the United States , 2003 .

[8]  Naomi L Kruhlak,et al.  A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals. , 2007, Regulatory toxicology and pharmacology : RTP.

[9]  S. Evans,et al.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports , 2001, Pharmacoepidemiology and drug safety.

[10]  W. Tong,et al.  Quantitative structure‐activity relationship methods: Perspectives on drug discovery and toxicology , 2003, Environmental toxicology and chemistry.

[11]  R. O’Neill,et al.  Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database , 2002, Drug safety.

[12]  J Ashby,et al.  Prediction of rodent carcinogenicity for 30 chemicals. , 1996, Environmental health perspectives.

[13]  J. Contrera,et al.  A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. , 1998, Regulatory toxicology and pharmacology : RTP.

[14]  G M Pearl,et al.  Integration of computational analysis as a sentinel tool in toxicological assessments. , 2001, Current topics in medicinal chemistry.

[15]  K. Hornbuckle,et al.  Evaluation of the Characteristics of Safety Withdrawal of Prescription Drugs from Worldwide Pharmaceutical Markets-1960 to 1999 , 2001 .

[16]  Naomi L Kruhlak,et al.  Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities. , 2009, Regulatory toxicology and pharmacology : RTP.

[17]  Naomi L Kruhlak,et al.  Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. , 2007, Regulatory toxicology and pharmacology : RTP.

[18]  L. Hall,et al.  Three new consensus QSAR models for the prediction of Ames genotoxicity. , 2004, Mutagenesis.

[19]  J. Hanley.  Receiver operating characteristic (ROC) methodology: the state of the art. , 1989, Critical reviews in diagnostic imaging.

[20]  R Benigni.  The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: definitive results. , 1997, Mutation research.

[21]  Naomi L Kruhlak,et al.  Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. , 2007, Advanced drug delivery reviews.

[22]  J Ashby,et al.  Prediction of rodent carcinogenicity for 44 chemicals: results. , 1994, Mutagenesis.

[23]  Alexander Golbraikh,et al.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection , 2002, J. Comput. Aided Mol. Des..

[24]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[25]  J. Senior,et al.  Drug-related hepatotoxicity. , 2006, The New England journal of medicine.

[26]  Frantz Thiessard,et al.  The history of disproportionality measures (reporting odds ratio, proportional reporting rates) in spontaneous reporting of adverse drug reactions , 2005, Pharmacoepidemiology and drug safety.

[27]  L B Lusted,et al.  Signal detectability and medical decision-making. , 1971, Science.

[28]  Sean Ekins.  Computational toxicology : risk assessment for pharmaceutical and environmental chemicals , 2007 .

[29]  Philip Howard,et al.  Practical considerations on the use of predictive models for regulatory purposes. , 2005, Environmental science & technology.

[30]  M. Fielden,et al.  A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity , 2005, Toxicologic pathology.

[31]  Edwin J Matthews,et al.  In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects , 2007, Expert opinion on drug metabolism & toxicology.

[32]  D. Bristol,et al.  The NIEHS Predictive-Toxicology Evaluation Project. , 1996, Environmental health perspectives.

[33]  N. Kruhlak,et al.  An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods. , 2006, Regulatory toxicology and pharmacology : RTP.

[34]  R. Benigni.  Structure-activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches. , 2005, Chemical reviews.

[35]  W. DuMouchel,et al.  Comparative Performance of Two Quantitative Safety Signalling Methods , 2006, Drug safety.

[36]  R. Saracci,et al.  Describing the validity of carcinogen screening tests. , 1979, British Journal of Cancer.

[37]  H. Zimmerman,et al.  Hepatotoxicity: The adverse effects of drugs and other chemicals on the liver , 1978 .

[38]  Joseph M. Tonning,et al.  Pharmacovigilance in the 21st Century: New Systematic Tools for an Old Problem , 2004, Pharmacotherapy.