Development of an approach for ab initio estimation of compound-induced liver injury based on global gene transcriptional profiles
| Autor: | Xudong, Dai, Yudong D, He, Hongyue, Dai, Pek Y, Lum, Christopher J, Roberts, Jeffrey F, Waring, Roger G, Ulrich |
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| Rok vydání: | 2007 |
| Předmět: |
Dose-Response Relationship
Drug Drug-Related Side Effects and Adverse Reactions Transcription Genetic Gene Expression Profiling Alanine Transaminase Bilirubin Models Biological Sensitivity and Specificity Toxicogenetics Rats Rats Sprague-Dawley Cholesterol Liver Pharmaceutical Preparations Artificial Intelligence Chemistry Clinical Animals Cluster Analysis Feasibility Studies Aspartate Aminotransferases Algorithms |
| Zdroj: | Genome informatics. International Conference on Genome Informatics. 17(2) |
| ISSN: | 0919-9454 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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