Exploiting the Japanese Toxicogenomics Project for Predictive Modelling of Drug Toxicity
Sprache des Titels:
CAMDA 2012, Satellite Meeting of ISMB/ECCB 2012
In the last decade, surprisingly few drugs reached the market. Many promising drug candidates
(approx. 80%) failed during or after Phase I, inter alia, due to issues with undetected toxicity .
The problem of undetected toxicity becomes even more apparent in the context of drug-induced
illness which causes approximately 100,000 deaths per year solely in the USA . Toxicogenomics
tries to avoid such problems by prioritizing less toxic drugs over more toxic ones in early drug
discovery. To this end, toxicogenomics employs high throughput molecular profiling technologies
and predicts the toxicity of drug candidates. For this prediction, large-scale -omics studies of
drug treated cell-lines and/or pharmacology model organisms are necessary. However, data exploitation
of such large-scale studies requires a highly optimized analysis pipeline, that provides
methods for correction of batch effects, noise reduction, dimensionality reduction, normalization,
summarization, filtering and prediction. In this work, we present a novel pipeline for the analysis of large-scale data sets in particular for
transcriptomics data. Our pipeline was tested on the Japanese Toxicogenomics Project (TGP) ,
where we evaluated to what degree in vitro bioassays can be used to predict in vivo responses.
The evaluation tasks were to predict drug induced liver injury (DILI) concern  and the most
prevalent in vivo pathological findings from the summarized in vitro gene expression values.