Development of an Approach for Ab Initio Estimation of Compound-Induced Liver Injury Based on Global Gene Transcriptional Pro les

Toxicity is a major cause of failure in drug development. A toxicogenomic approach may provide a powerful tool for better assessing the potential toxicity of drug candidates. Several approaches have been reported for predicting hepatotoxicity based on reference compounds with well-studied toxicity mechanisms. We developed a new approach for assessing compound-induced liver injury without prior knowledge of a compound's mechanism of toxicity. Using samples from rodents treated with 49 known liver toxins and 10 compounds without known liver toxicity, we derived a hepatotoxicity score as a single quantitative measurement for assessing the degree of induced liver damage. Combining the sensitivity of the hepatotoxicity score and the power of a machine learning algorithm, we then built a model to predict compound-induced liver injury based on 212 expression pro les. As estimated in an independent data set of 54 expression pro les, the built model predicted compound-induced liver damage with 90.9% sensitivity and 88.4% speci city. Our ndings illustrate the feasibility of ab initio estimation of liver toxicity based on transcriptional pro les.