Mapping drug physico-chemical features to pathway activity reveals molecular networks linked to toxicity outcome.
The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology.
Antczak
Philipp
P
School of Biosciences, University of Birmingham, Birmingham, United Kingdom.
Ortega
Fernando
F
Chipman
J Kevin
JK
Falciani
Francesco
F
D000818:Animals; D002450:Cell Communication / Q000187:drug effects; D055598:Chemical Phenomena; D016000:Cluster Analysis; D019295:Computational Biology; D055808:Drug Discovery; D064420:Drug-Related Side Effects and Adverse Reactions; D020869:Gene Expression Profiling; D007684:Kidney Tubules / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D010641:Phenotype; D051381:Rats; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000187:drug effects; D014158:Transcription, Genetic
D004364:Pharmaceutical Preparations
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