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

Toxicological Sciences

Volumn 136, Issue 1, 2013, Pages 242-249

  Chen, Minjun ^a   Hong, Huixiao ^a   Fang, Hong ^a   Kelly, Reagan ^a   Zhou, Guangxu ^a   Borlak, Jürgen ^b   Tong, Weida ^a  

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

^b HANNOVER MEDICAL SCHOOL   (Germany)

Author keywords

Drug label; Drug induced liver injury; External validation.; Predictive model; Quantitative structure activity relationship; Therapeutic categories

Indexed keywords

AGENTS ACTING ON THE AUDITORY AND VESTIBULAR SYSTEMS; ANABOLIC AGENT; ANALGESIC AGENT; ANESTHETIC AGENT; ANTIBIOTIC AGENT; ANTICOAGULANT AGENT; ANTIDIARRHEAL AGENT; ANTIFUNGAL AGENT; ANTIHISTAMINIC AGENT; ANTIHYPERTENSIVE AGENT; ANTIINFECTIVE AGENT; ANTIINFLAMMATORY AGENT; ANTINEOPLASTIC AGENT; ANTIPROTOZOAL AGENT; ANTIPRURITIC AGENT; ANTIRHEUMATIC AGENT; ANTIVIRUS AGENT; CALCIUM CHANNEL BLOCKING AGENT; CARDIAC AGENT; CENTRAL STIMULANT AGENT; CONTRAST MEDIUM; CORTICOSTEROID; EYE DROPS; GASTROINTESTINAL AGENT; MUSCLE RELAXANT AGENT; NEUROLEPTIC AGENT; PERIPHERAL VASODILATING AGENT; SEX HORMONE; UNINDEXED DRUG; VITAMIN;

ARTICLE; DRUG APPROVAL; DRUG INDUSTRY; DRUG LABELING; FOOD AND DRUG ADMINISTRATION; HUMAN; PREDICTIVE VALUE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RISK; TOXIC HEPATITIS;

DRUG LABEL; DRUG-INDUCED LIVER INJURY; EXTERNAL VALIDATION.; PREDICTIVE MODEL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; THERAPEUTIC CATEGORIES;

COMPUTER SIMULATION; DRUG APPROVAL; DRUG LABELING; DRUG-INDUCED LIVER INJURY; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; MODELS, MOLECULAR; MOLECULAR STRUCTURE; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; UNITED STATES; UNITED STATES FOOD AND DRUG ADMINISTRATION;

EID: 84887907555     PISSN: 10966080     EISSN: 10960929     Source Type: Journal    
DOI: 10.1093/toxsci/kft189     Document Type: Article

References (37)

1. Arrowsmith, J. (2011a). Trial watch: Phase II failures: 2008-2010. Nat. Rev. Drug Discov. 10, 328-329.
2. Arrowsmith, J. (2011b). Trial watch: Phase III and submission failures: 2007-2010. Nat. Rev. Drug Discov. 10, 87.
3. Ballet, F. (1997). Hepatotoxicity in drug development: Detection, significance and solutions. J. Hepatol. 26(Suppl. 2), 26-36.
4. Chen, M., Borlak, J., and Tong, W. (2013a). High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury. Hepatology 58, 388-396.
5. Chen, M., Shi, L., Kelly, R., Perkins, R., Fang, H., and Tong, W. (2011b). Selecting a single model or combining multiple models for microarray-based classifier development?-A comparative analysis based on large and diverse datasets generated from the MAQC-II project. BMC Bioinformatics 12, S3.
6. Chen, M., Vijay, V., Shi, Q., Liu, Z., Fang, H., and Tong, W. (2011a). FDAapproved drug labeling for the study of drug-induced liver injury. Drug Discov. Today 16, 697-703.
7. Chen, M., Zhang, J., Wang, Y., Liu, Z., Kelly, R., Zhou, G., Fang, H., Borlak, J., and Tong, W. (2013b). Liver Toxicity Knowledge Base (LTKB)-A systems approach to a complex endpoint. Clin. Pharmacol. Therapeut. 95, 409-412.
8. Chen, M., Zhang, M., Borlak, J., and Tong, W. (2012). A decade of toxicogenomic research and its contribution to toxicological science. Toxicol. Sci. 130, 217-228.
9. Ekins, S., Williams, A. J., and Xu, J. J. (2010). A predictive ligand-based Bayesian model for human drug induced liver injury. Drug Metab. Dispos. 38, 2302-2308.
10. Fontana, R. J. (2008). Acute liver failure due to drugs. Semin. Liver Dis. 28, 175-187.
11. Greene, N., Fisk, L., Naven, R. T., Note, R. R., Patel, M. L., and Pelletier, D. J. (2010) Developing structure-activity relationships for the prediction of hepatotoxicity. Chem. Res. Toxicol. 23, 1215-1222.
12. Hamburg, M. A. (2011). Advancing regulatory science. Science. 331, 987.
13. Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L., Su, Z., Perkins, R., and Tong, W. (2008). Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J. Chem. Inf. Model. 48, 1337-1344.
14. Kaplowitz, N. (2013). Avoiding idiosyncratic DILI: Two is better than one. Hepatology 58, 15-17.
15. Kola, I., and Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711-715.
16. Leeson, P. D., and Springthorpe, B. (2007). The influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discov. 6, 881-890.
17. Liu, Z., Shi, Q., Ding, D., Kelly, R., Fang, H., and Tong, W. (2011). Translating clinical findings into knowledge in drug safety evaluation-Drug induced liver injury prediction system (DILIps). PLoS Comput. Biol. 7, e1002310.
18. Meanwell, N. A. (2011). Improving drug candidates by design: A focus on physicochemical properties as a means of improving compound disposition and safety. Chem. Res. Toxicol. 24, 1420-1456.
19. Merlot, C. (2010) Computational toxicology-A tool for early safety evaluation. Drug Discov. Today 15, 16-22.
20. Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Muller, L., and Pahler, A. (2008). Computational toxicology in drug development. Drug Discov. Today 13, 303-310.
21. Netzeva, T. I., Worth, A., Aldenberg, T., Benigni, R., Cronin, M. T., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., et al. (2005). Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern. Lab. Anim. 33, 155-173.
22. Ostapowicz, G., Fontana, R. J., Schiodt, F. V., Larson, A., Davern, T. J., Han, S. H., McCashland, T. M., Shakil, A. O., Hay, J. E., Hynan, L., et al. (2002). Results of a prospective study of acute liver failure at 17 tertiary care centers in the United States. Ann. Intern. Med. 137, 947-954.
23. Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., and Schacht, A. L. (2010). How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203-214.
24. Przybylak, K. R., and Cronin, M. T. (2012). In silico models for druginduced liver injury-Current status. Expert Opin. Drug Metab. Toxicol. 8, 201-217.
25. R Development Core Team. (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at: http://www.R-project.org/.
26. Rodgers, A. D., Zhu, H., Fourches, D., Rusyn, I., and Tropsha, A. (2010). Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method. Chem. Res. Toxicol. 23, 724-732.
27. Rusyn, I., Sedykh, A., Low, Y., Guyton, K. Z., and Tropsha, A. (2012). Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicol. Sci. 127, 1-9.
28. Thomas, C. E., and Will, Y. (2012). The impact of assay technology as applied to safety assessment in reducing compound attrition in drug discovery. Expert Opin. Drug Discov. 7, 109-122.
29. Tong, W., Hong, H., Fang, H., Xie, Q., and Perkins, R. (2003). Decision forest: Combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 43, 525-531.
30. Tong, W., Xie, Q., Hong, H., Shi, L., Fang, H., and Perkins, R. (2004a). Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity. Environ. Health Perspect. 112, 1249-1254.
31. Tong, W., Xie, Q., Hong, H., Shi, L., Fang, H., Perkins, R., and Petricoin, E. F. (2004b). Using decision forest to classify prostate cancer samples on the basis of SELDI-TOF MS data: Assessing chance correlation and prediction confidence. Environ. Health Perspect. 112, 1622-1627.
32. Tropsha, A., and Golbraikh, A. (2007). Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13, 3494-3504.
33. Watkins, P. B. (2011). Drug safety sciences and the bottleneck in drug development. Clin. Pharmacol. Ther. 89, 788-790.
34. Willy, M. E., and Li, Z. (2004). What is prescription labeling communicating to doctors about hepatotoxic drugs? A study of FDA approved product labeling. Pharmacoepidemiol. Drug Saf. 13, 201-206.
35. Xu, J. J., Henstock, P. V., Dunn, M. C., Smith, A. R., Chabot, J. R., and de Graaf, D. (2008). Cellular imaging predictions of clinical drug-induced liver injury. Toxicol. Sci. 105, 97-105.
36. Zhang, M., Chen, M., and Tong, W. (2012). Is toxicogenomics a more reliable and sensitive biomarker than conventional indicators from rats to predict drug-induced liver injury in humans? Chem. Res. Toxicol. 25, 122-129.
37. Zimmerman, H. J. (1999). Hepatotoxicity: The Adverse Effects of Drugs and Other Chemicals on the Liver. Lippincott, Williams & Wilkins, Philadelphia, PA.