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

Regulatory Toxicology and Pharmacology

Volumn 54, Issue 1, 2009, Pages 23-42

  Matthews, Edwin J ^a   Ursem, Carling J ^a,b   Kruhlak, Naomi L ^a   Benz, R Daniel ^a   Sabaté, David Aragonés ^c   Yang, Chihae ^d   Klopman, Gilles ^e   Contrera, Joseph F ^f  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

^b GlobalNet Services   (United States)

^c Prous Institute for Biomedical Research   (Spain)

^d Leadscope Inc   (United States)

^e MULTICASE Inc   (United States)

^f Computational Toxicology Services LLC ^*  (United States)

Author keywords

AERS; Computational toxicology; Drug adverse effects; Drug induced liver injury; Drug induced renal toxicity; In silico; Post market reporting; QSAR software; Quantitative structure activity relationships; SAR; SRS

Indexed keywords

BENDAZAC; BENZARONE; BENZIODARONE; CYCLOFENIL; EBROTIDINE; EXIFONE; FIPEXIDE; IBUFENAC; ISAXONINE PHOSPHATE; NIALAMIDE; NITREFAZOLE; SULFACARBAMIDE; SULOCTIDIL; XENAZOIC ACID; ZIMELDINE;

ARTICLE; COMPUTER MODEL; COMPUTER PROGRAM; DATA BASE; DISEASE CLASSIFICATION; DRUG INDUCED DISEASE; DRUG SURVEILLANCE PROGRAM; FOOD AND DRUG ADMINISTRATION; HEPATITIS; HEPATOBILIARY DISEASE; HUMAN; JAUNDICE; LIVER TOXICITY; NEPHROTOXICITY; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; URINARY TRACT DISEASE; VALIDATION STUDY;

ADVERSE DRUG REACTION REPORTING SYSTEMS; BILIARY TRACT DISEASES; CLUSTER ANALYSIS; DATABASES, FACTUAL; EARLY DIAGNOSIS; ENDPOINT DETERMINATION; HEPATITIS, TOXIC; HUMANS; MODELS, BIOLOGICAL; PHARMACEUTICAL PREPARATIONS; PRODUCT SURVEILLANCE, POSTMARKETING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOFTWARE; UNITED STATES; UNITED STATES FOOD AND DRUG ADMINISTRATION; UROLOGIC DISEASES;

EID: 67349247530     PISSN: 02732300     EISSN: 10960295     Source Type: Journal    
DOI: 10.1016/j.yrtph.2009.01.009     Document Type: Article

References (45)

1. Almenoff J.S., LaCroix K.K., Yuen N.A., Fram D., and DuMouchel W. Comparative performance of two quantitative safety signaling methods. Drug Saf. 29 (2006) 875-887
2. Ashby J. Prediction of rodent carcinogenicity for 30 chemicals. Environ. Health Perspect. 104 (1996) 1101-1104
3. Ashby J., and Tennant R.W. Prediction of rodent carcinogenicity for 44 chemicals. Mutagenesis 9 (1994) 7-15
4. Bager Y., Lindebro M.C., Martel P., Chaumontet C., Warngard L., and Benigni R. The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: definitive results. 4. Prediction of non-lethal mammalian toxicological and points, and expert systems for toxicity prediction. Mutat. Res. 387 (1997) 35-45
5. Benigni R. Structure activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches. Chem. Rev. 105 (2005) 1767-1800
6. Benz R.D. Toxicological and clinical computational analysis and the US FDA/CDER. Expert Opin. Drug Metab. Toxicol. 3 (2007) 109-124
7. Bristol D.W., Wachsman J.T., and Greenwell A. The NIEHS predictive-toxicology evaluation project. Environ. Health Perspect. 104 (1996) 1001-1010
8. Contrera J.F., Kruhlak N.L., Matthews E.J., and Benz R.D. Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR Models. Regul. Toxicol. Pharmacol. 49 (2007) 172-182
9. Cooper J.A., Saracci R., and Cole P. Br. J. Cancer 39 (1979) 87-89
10. COSTART. 1995. Coding Symbols for Thesaurus of Adverse Reaction Terms, fifth ed., Department of Health and Human Services: Rockville, MD.
11. DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System. Am. Stat. 53 (1999) 177-202
12. Evans S.J., Waller P.C., and Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidem. Drug Saf. 10 (2001) 483-486
13. Fielden M.R., Eynon B.P., Natsoulis G., Jarnagin K., Banas D., and Kolaja K.L. A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicol. Pathol. 33 (2005) 675-683
14. Fung M., Thornton A., Mybeck K., Wu J.H., Hornbuckle K., and Muniz E. Evaluation of the characteristics of safety withdrawal of prescription drugs from worldwide pharmaceutical markets-1960 to 1999. Drug Inform. J. 25 (2001) 293-317
15. Golbraikh A., and Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J. Comput. Aided Mol. Des. 16 (2002) 357-369
16. Gombar V.K., Mattioni B.E., Zwickl C., and Deahl T. Computational Approaches for Assessment of Toxicity-A Historical Perspective and Current Status. In: Elkins S. (Ed). Computational Toxicology: Risk Assessment for Pharmaceuticals and Environmental Chemicals (2007), John Wiley & Sons, Inc., Hoboken, NJ 183-215
17. Hanley J.A. Receiver operating characteristic (ROC) methodology: the state of the art. Crit. Rev. Diagn. Imaging 29 (1989) 307-335
18. Kruhlak N.L., Contrera J.F., Benz R.D., and Matthews E.J. Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. Adv. Drug Deliv. Rev. 59 (2007) 43-55
19. Lee W.M. Acute liver failure in the United States. Semin. Liver Dis. 23 (2003) 217-226
20. Lee, W.M., 2005. Drug-induced acute liver failure in the US 2005: results from the US acute liver failure study group. Available from: .
21. Lusted L.B. Signal detectability and medical decision-making. Science 171 (1971) 1217-1219
22. Matthews E.J., and Contrera J.F. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul. Toxicol. Pharmacol. 28 (1998) 242-264
23. Matthews E.J., and Contrera J.F. In silico approaches to explore toxicity endpoints: Issues and concerns for estimating human health effects. Expert Opin. Drug Metabol. Toxicol. 3 (2007) 125-134
24. Matthews E.J., Kruhlak N.L., Benz R.D., Contrera J.F., and Marchant C.A. 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. Toxicol. Mechan. Methods 18 (2008) 189-206
25. Matthews, E.J., Kruhlak, N.L., Benz, R.D., Contrera, J.F., Marchant, C.A., David Aragonés Sabaté. 2009. Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: C: Use of (Q)SAR and Expert systems for estimation of the mechanism of action of drug-induced urinary tract and hepatobiliary toxicities. Regul. Toxicol. Pharmacol, in press, doi:10.1016/j.yrtph.2009.01.007.
26. Matthews E.J., Kruhlak N.L., Benz R.D., and Contrera J.F. A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database. Regul. Toxicol. Pharmacol. 47 (2007) 115-135
27. Matthews E.J., Kruhlak N.L., Benz R.D., Ivanov J., Klopman G., and Contrera J.F. A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals. Regul. Toxicol. Pharmacol. 47 (2007) 136-155
28. Matthews E.J., Kruhlak N.L., Cimino M.C., Benz R.D., and Contrera J.C. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul. Toxicol. Pharmacol. 44 (2006) 83-96
29. Matthews E.J., Kruhlak N.L., Cimino M.C., Benz R.D., and Contrera J.F. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods. Regul. Toxicol. Pharmacol. 44 (2006) 97-110
30. Matthews E.J., Kruhlak N.L., Weaver J.L., Benz R.D., and Contrera J.F. Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling. Curr. Drug Discov. Technol. 1 (2004) 243-254
31. Moore N., Thiessard F., and Begaud B. The history of disproportionality measures (reporting odds ratio, proportional reporting rates) in spontaneous reporting of adverse drug reactions. Pharmacoepidemiol. Drug Saf. 14 (2005) 285-286
32. Navarro V.J., and Senior J.R. Drug related hepatotoxicity. N. Engl. J. Med. 354 (2006) 731-739
33. OECD, 2007. Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. OECD environment health and safety publications series on testing and assessment No. XX. Draft.
34. Pearl G.M., Livingston-Carr S., and Durham S.K. Integration of computational analysis as a sentinel tool in toxicological assessments. Curr. Top. Med. Chem. 1 (2001) 247-255
35. Perkins R., Fang H., Tong W., and Welsh W.J. Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology. Environ. Toxicol. Chem. 22 (2003) 1666-1679
36. Provost F., and Fawcett T. Robust classification for imprecise environments. Machine Learn. J. 42 (2001) 203-231
37. Szarfman A., Machado S.G., and O'Neill R.T. 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. Drug Saf. 25 (2003) 381-392
38. Szarfman A., Tonning J.M., and Doraiswamy P.M. Pharmacovigilance in the 21st century: new systematic tools for an old problem. Pharmacotherapy 24 (2004) 1099-1104
39. Temple, R.J., 2001. Hepatotoxicity through the years: impact on the FDA. Available from: .
40. Tunkel J., Mayo K., Austin C., Hickerson A., and Howard P. Practical considerations on the use of predictive models for regulatory purposes. Environ. Sci. Technol. 39 (2005) 2188-2199
41. Ursem, C.J., Matthews, E.J., Kruhlak, N.L., Contrera, J.F., Benz, R.D., 2009. Identification of structure activity relationships for adverse effects of pharmaceuticals in humans A: Use of FDA post market reports to create a database of hepatobiliary and urinary tract toxicities. Regul. Toxicol. Pharmacol, in press, doi:10.1016/j.yrtph.2008.12.009
42. US EPA, Office of Pollution Prevention and Toxics, 1999. The use of structure-activity relationships (SAR) in the high production volume chenmicals challenge program. Available from: .
43. Votano J.R., Parham M., Hall L.H., Kier L.B., Oloff S., and Tropsha A. 3 new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis 19 (2004) 365-377
44. Zimmerman H.J. Drug-induced liver disease. Drugs 16 (1978) 25-45
45. Zimmerman H.J. Hepatotoxicity: The Adverse Effects of Drugs and Other Chemicals on the Liver. second ed. (1999), Lipincott Williams and Wilkins, Philadelphia