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Using the ToxMiner Database for a Network Analysis of Linkage between ToxCast Phase I Chemicals and Thyroid Related Disease Outcomes
Citation:
MORTENSEN, H. M., D. J. DIX, K. A. HOUCK, R. J. KAVLOCK, AND R. JUDSON. Using the ToxMiner Database for a Network Analysis of Linkage between ToxCast Phase I Chemicals and Thyroid Related Disease Outcomes. Presented at ToxCast Data Analysis Summit, RTP, NC, May 14 - 15, 2009.
Impact/Purpose:
Here we explore the enrichment of the ToxCast HTS dataset for thyroid related outcomes, drawing from published data on genes related to thyroid phenotypes, and characterize the structure and linkage between the ToxCast chemical set and thyroid cancer and related etiologies using a Bayesian analysis framework.
Description:
The US EPA ToxCast program is using in vitro, high-throughput screening (HTS) to profile and model the bioactivity of environmental chemicals. The main goal of the ToxCast program is to generate predictive signatures of toxicity that ultimately provide rapid and cost-effective methods to prioritize chemicals for targeted in vivo testing, thus improving efficiency of the use of animals in those bioassays. The chemicals selected for Phase I are composed largely of a diverse set of pesticide active ingredients, which had sufficient supporting in vivo data included as part of their registration process with the EPA. These were supplemented with a number of non-pesticide, high-production volume chemicals of environmental concern. Application of HTS to environmental toxicants is a novel approach to predictive toxicology, and differs from what is required for drug efficacy screening in several ways. Biochemical interaction of environmental chemicals are generally weaker than that seen with drugs and their intended targets. Additionally, the chemical diversity space covered by environmental chemicals is much broader compared to that of pharmaceuticals. The ToxMinerTM database was created in order to link biological, metabolic, and cellular pathway data to genes and in vitro assay data for the chemicals screened in the ToxCast Phase I HTS assays. Also included in ToxMiner was human disease information, which correlated with ToxCast assays that target specific genetic loci. We have implemented initial pathway inference and network analyses, which allow linkage of the types of adverse health outcomes with exposure to chemicals screened in Phase I. Initial pathway analyses, in conjunction with statistical inference, has indicated the prevalence of thyroid cancer and thyroid-related pathologies as outcomes of exposure to those chemicals. Here we explore the enrichment of the ToxCast HTS dataset for thyroid related outcomes, drawing from published data on genes related to thyroid phenotypes, and characterize the structure and linkage between the ToxCast chemical set and thyroid cancer and related etiologies using a Bayesian analysis framework. Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.