Classification of Chemicals Based on Concentration-Dependent Toxicological Data Using ToxClust

Abstract

Concentration-dependent response relationships provide essential information on the characteristics of chemicalinduced effects on toxicological end points, which include effect (inhibition or induction), potency, and efficacy of the chemical. Recent developments in systems biology and high throughput technologies 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 concentrationresponse data derived with single or multiple end points. This method utilizes a least distance-searching algorithm (LDSA) to measure the pattern dissimilarity of concentrationresponse 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. IntroductionThere is urgent need for methods that allow evaluation of large numbers chemicals for their potential toxicity and to prioritize these chemicals for further testing (1). Recent developments in toxicogenomics and high-throughput screening techniques, including among others those broadly defined as genomics, transcriptomics, proteomics, and metabonomics utilizing multiple molecular and cellular end points has enabled testing of large numbers of chemicals (2). These new techniques are increasingly being used in priority chemical screening programs such as Tier 1 of the Endocrine Disruptor Screening Program of U.S. Environmental Protection Agency (EPA) (1). During these screening initiatives, researchers collect large amounts of multidimensional data (e.g., gene transcripts, proteins, and enzyme products) for various concentrations of each chemical analyzed (2). One of the major remaining challenges is the prioritization of chemicals for further testing based on the information obtained during Tier I (3). However, because of the lack of appropriate data evaluation techniques that effectively handle large quantities of diverse data, progress in the classification of chemicals by mode-of-action has been slow and is not yet part of many regulatory programs entrusted with this action (4). To accurately classify chemicals based on their critical mechanism of action so that they can be grouped for risk assessments (2) and to take advantage of detailed information on a few model chemicals, we must know the mechanism of action. How...

Mixture Effects in Ecotoxicology

“…More recently, the Verhaar methods have been revisited, and improved rules were proposed (Enoch et al 2008). Based on the same concept is ToxClust that allows clustering of chemicals using concentration-response data of single or multiple endpoints and derives a pattern dissimilarity of concentration-response curves between chemicals and their relative toxic potency (Zhang et al 2009). The Verhaar classification has been implemented in the public domain software ToxTree (http:// toxtree.sourceforge.net/).…”

Toxicology of Water

“…Concentration (dose)-response relationships of endpoints at the molecular and cellular level provide essential mechanistic information for the toxicity of each tested chemical. To efficiently manage and interpret these large datasets, a novel computational toxicology program, ToxClust, was developed which can cluster chemicals based on concentration-response data derived with single or multiple endpoints [49].…”

Estimation of the Mechanism of Adrenal Action of Endocrine-Disrupting Compounds Using a Computational Model of Adrenal Steroidogenesis in NCI-H295R Cells

“…To systematically understand how exogenous compounds affect adrenal steroidogenesis, simultaneous determination of all detectable steroid hormones and integrative analysis of these complex data would be important. As an exploratory approach to analyze complex data, ToxClust developed by Zhang and colleagues in 2009 is able to visualize concentration-dependent response relationships in the characteristics of chemically induced toxicological effects [ 20 ]. However, this exploratory approach is unable to provide a quantitative understanding of the mechanism of action of adrenal toxicants or reveal systematic information about the effect of each enzymatic reaction, interactions, and feedback in the adrenal steroidogenesis pathway.…”