Skip Navigation




    The US Food and Drug Administration, Center for Drug Evaluation and Research     (FDA/CDER) and Critical Path Institute (C-Path) co-sponsor an annual Drug-Induced Liver Injury Conference.  The program is endorsed by the National Institutes of Health, Drug-Induced Liver Injury Network (DILIN), American Association for the Study of Liver Diseases (AASLD), Hamner-UNC Institute for Drug Safety Sciences, and the Pharmaceutical Research and Manufacturers of America (PhRMA).  The academic-industry-government Conference provides a forum to discuss new findings and thinking about DILI, including presentation by experts in clinical hepatology, toxicology, and other scientific fields.  Presentations from these conferences can be found on the AASLD website.

  • Liver Toxicity Knowledge Base (LTKB), National Center for Toxicological Research
    Liver Toxicity Knowledge Base (LTKB) Benchmark Dataset
    Liver Toxicity Knowledge Base (LTKB) Publications
    U.S. Food and Drug Administration
  • DILIsym® and the DILI-sim Initiative
    University of North Carolina Institute for Drug Safety Sciences
  • Endocrine Disruptor Knowledge Base (EDKB), National Center for Toxicological Research
    U.S. Food and Drug Administration
  • Virtual Liver Project (v-Liver™)
    National Center for Computational Toxicology, U.S. Environmental Protection Agency
  • Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chem Res Toxicol 2010; 23: 171-83. PubMed Citation
  • Liu Z, Shi Q, Ding D, Kelly R, Fang H, Tong W. Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps). PLoS Comput Biol 2011; 7. PubMed Citation
  • Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz'min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem Res Toxicol 2011; 24: 1251-62. PubMed Citation
  • Myshkin E, Brennan R, Khasanova T, Sitnik T, Serebriyskaya T, Litvinova E, Guryanov A, et al. Prediction of organ toxicity endpoints by QSAR modeling based on precise chemical-histopathology annotations. Chem Biol Drug Des 2012; 80: 406-16. PubMed Citation
  • Przybylak KR, Cronin MT. In silico models for drug-induced liver injury--current status. Expert Opin Drug Metab Toxicol 2012; 8: 201-17. PubMed Citation
  • Zhang M, Chen M, Tong W. Is toxicogenomics a more reliable and sensitive biomarker than conventional indicators from rats to predict drug-induced liver injury in humans? Chem Res Toxicol 2012; 25: 122-9. PubMed Citation
  • Chen M, Hong H, Fang H, Kelly R, Zhou G, Borlak J, Tong W. 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. Toxicol Sci 2013; 136: 242-9. PubMed Citation
  • Davis AP, Murphy CG, Johnson R, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, et al. The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res 2013; 41. PubMed Citation
  • Rodgers AD, Zhu H, Fourches D, Rusyn I, Tropsha A. Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method. Chem Res Toxicol 2010; 23: 724-32. PubMed Citation
  • Xing L, Wu L, Liu Y, Ai N, Lu X, Fan X. LTMap: a web server for assessing the potential liver toxicity by genome-wide transcriptional expression data. J Appl Toxicol 2013. PubMed Citation
  • Zhang JD, Berntenis N, Roth A, Ebeling M. Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity. Pharmacogenomics J 2013. PubMed Citation
  • Shoda LK, Woodhead JL, Siler SQ, Watkins PB, Howell BA. Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug-induced liver injury. Biopharm Drug Dispos 2014; 35: 33-49. PubMed Citation
  • Zhu XW, Sedykh A, Liu SS. Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information. J Appl Toxicol 2014; 34: 281-8. PubMed Citation


Top of page


PDF documents are best viewed with the free Adobe® Reader.