title: Development of an approach for ab initio estimation of compound-induced liver injury based on global gene transcriptional profiles.
abstract: 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 profiles. As estimated in an independent data set of 54 expression profiles, the built model predicted compound-induced liver damage with 90.9% sensitivity and 88.4% specificity. Our findings illustrate the feasibility of ab initio estimation of liver toxicity based on transcriptional profiles.
authors.affiliation: Rosetta Inpharmatics LLC, Merck & Co., Inc., Seattle, WA 98109, USA. xudong_dai@merck.com
authors.forename: Xudong
authors.initials: X
authors.lastname: Dai
authors.forename: Yudong D
authors.initials: YD
authors.lastname: He
authors.forename: Hongyue
authors.initials: H
authors.lastname: Dai
authors.forename: Pek Y
authors.initials: PY
authors.lastname: Lum
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Roberts
authors.forename: Jeffrey F
authors.initials: JF
authors.lastname: Waring
authors.forename: Roger G
authors.initials: RG
authors.lastname: Ulrich
mesh_terms: D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001219:Aspartate Aminotransferases / Q000097:blood; D001663:Bilirubin / Q000097:blood; D002624:Chemistry, Clinical / Q000379:methods; D002784:Cholesterol / Q000097:blood; D016000:Cluster Analysis; D004305:Dose-Response Relationship, Drug; D064420:Drug-Related Side Effects and Adverse Reactions / Q000139:chemically induced / Q000145:classification / Q000378:metabolism; D005240:Feasibility Studies; D020869:Gene Expression Profiling; D008099:Liver / Q000187:drug effects* / Q000201:enzymology / Q000473:pathology; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000145:classification; D051381:Rats; D017207:Rats, Sprague-Dawley; D012680:Sensitivity and Specificity; D043922:Toxicogenetics / Q000379:methods*; D014158:Transcription, Genetic
chemical_list: D004364:Pharmaceutical Preparations; D002784:Cholesterol; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase; D001663:Bilirubin