Classification of chemicals based on concentration-dependent toxicological data using ToxClust

Concentration-dependent response relationships provide essential information on the characteristics of chemical-induced effects on toxicological end points, which include effect (inhibition or induction), potency, and efficacy of the chemical. Recent developments in systems biology and high throughputtechnologies have allowed simultaneous examination of many chemicals at multiple end point levels. While this increase in the quantity of information generated offers great potential, it also poses a significant challenge to environmental scientists to efficiently manage and interpret these large data sets. Here we present a novel method, ToxClust, that allows clustering of chemicals on the basis of concentration-response data derived with single or multiple end points. This method utilizes a least distance-searching algorithm (LDSA) to measure the pattern dissimilarity of concentration-response curves between chemicals and their relative toxic potency. ToxClust was tested using simulated data and chemical test data collected from the human H295R cell-based in vitro steroidogenesis assay. ToxClust effectively identified similar patterns of simulated data and responses to the exposure with the five model chemicals and separated them into different groups on the basis of their dissimilarities. These observations demonstrate that ToxClust not only provides an effective data analysis and visualization tool, but also has value in hypothesis generation and mechanism-based chemical classification.