Publications : 2015

Hester S, Eastmond DA, Bhat VS. 2015. Developing toxicogenomics as a research tool by applying benchmark dose-response modeling to inform chemical mode of action and tumorigenic potency. Invited Contribution, Special Issue on Toxicogenomics: The emergence of a new research and regulatory paradigm. Inter J Biotechnol 14:28-46.

Abstract

Results of global gene expression profiling after short-term exposures can be used to inform tumorigenic potency and chemical mode of action (MOA) and thus serve as a strategy to prioritize future or data-poor chemicals for further evaluation. This compilation of case studies uses benchmark dose-response modeling to estimate low-dose potency for activation of key genes and pathways. The transcriptional benchmark dose (BMDT) estimates were compared to benchmark dose estimates for more traditional toxicological (i.e., apical) responses (BMDA) at the cellular or tissue level. In addition to potency, temporality was also informed by comparing short-term BMDT and BMDA to chronic BMDA for adverse outcomes that might traditionally be used as the basis for quantitative risk assessment. The case studies included liver gene expression at 30 days for conazole pesticides, some of which are murine hepatocarcinogens, liver gene expression at ≤7 days for prototype nuclear receptor (CAR and PPAR), nongenotoxic rodent hepatocarcinogens, and urinary bladder gene expression at ≤ 20 weeks for diuron, a substituted urea pesticide associated with urinary bladder cytotoxicity and tumorigenesis in rats. Our studies showed that key gene and pathway-level BMDT were highly concordant and phenotypically anchored to BMDA for target tissue responses. Our short-term BMDT were also consistent with potency estimates for the tumorigenic outcome or precursor key events such as hyperplasia. By encompassing multiple rodent species, target tissues, MOA, chemical classes, and exposure durations, this approach illustrates how toxicogenomics as a research tool can help develop more efficient chemical testing and prioritization strategies.