title: In silico models for the prediction of dose-dependent human hepatotoxicity.
abstract: The liver is extremely vulnerable to the effects of xenobiotics due to its critical role in metabolism. Drug-induced hepatotoxicity may involve any number of different liver injuries, some of which lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important dose-limiting considerations in the drug development cycle, yet there remains a serious shortage of methods to predict hepatotoxicity from chemical structure. We discuss our latest findings in this area and present a new, fully general in silico model which is able to predict the occurrence of dose-dependent human hepatotoxicity with greater than 80% accuracy. Utilizing an ensemble recursive partitioning approach, the model classifies compounds as toxic or non-toxic and provides a confidence level to indicate which predictions are most likely to be correct. Only 2D structural information is required and predictions can be made quite rapidly, so this approach is entirely appropriate for data mining applications and for profiling large synthetic and/or virtual libraries.
authors.affiliation: ADMET R&D, Accelrys, CN5375, Princeton, NJ 08543-5375, USA. cheng189@adelphia.net
authors.forename: Ailan
authors.initials: A
authors.lastname: Cheng
authors.forename: Steven L
authors.initials: SL
authors.lastname: Dixon
mesh_terms: D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008099:Liver / Q000187:drug effects*; D008954:Models, Biological; D009010:Monte Carlo Method; D014116:Toxicology; D015262:Xenobiotics / Q000633:toxicity
chemical_list: D015262:Xenobiotics