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

Xudong Dai[1] (xudong_dai@merck.com)
Yudong D. He[1] (yudong_he@merck.com)
Hongyue Dai[1] (Hongyue_dai@merck.com)
Pek Y. Lum[1] (pek_lum@merck.com)
Christopher J. Roberts[1] (Robchris@merck.com)
Jeffrey F. Waring[2] (Jeff.waring@abbott.com)
Roger G. Ulrich[1] (Roger_ulrich@merck.com)

[1]Rosetta Inpharmatics LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, WA 98109 USA
[2]Abbott Laboratories, Abbott Park, IL 60064 USA


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.

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Japanese Society for Bioinformatics