Developing toxicogenomics as a research tool by applying benchmark dose-response modelling to inform chemical mode of action and tumorigenic potency. (2015)

• Record Type: Journal Article
• Authors: Hester, Susan; Eastmond, David A.; Bhat, Virunya S.
• Is Part Of: International journal of biotechnology. Volume 14 Number 1(2015)
• Page Start: 28
• Page End: 46
• Publication Date: 2015
• Subjects: gene expression profiling -- risk assessment -- transcriptional benchmark dose -- dose response modelling -- benchmark dose -- chemical MOA -- mode of action -- phenotypic anchoring -- thresholds -- toxicogenomics -- tumorigenic potency -- hyperplasia -- chemical testing -- prioritisation strategies -- precursor key events -- liver -- conazole pesticides -- prototype nuclear receptors -- hepatocarcinogens -- diuron -- urea pesticides -- urinary bladder cytotoxicity -- multiple rodent species; Biotechnology -- Economic aspects -- Periodicals; Biotechnology -- Research -- Periodicals; 338.47660605
• Languages: English
• ISSNs: 0963-6048

Abstract

Global expression profiling of short-term exposures can inform chemical mode of action (MOA), temporality of key events, and tumorigenic potency. In this compilation of case studies, transcriptional benchmark dose (BMDT) estimates for activation of key genes and pathways after short-term exposures were consistent with and thus phenotypically anchored to potency estimates for the tumorigenic outcome or precursor key events such as hyperplasia. The case studies included liver gene expression at ≤ 30 days for conazole pesticides and prototype nuclear receptor (CAR and PPARα) non-genotoxic 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. 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 prioritisation strategies for future data-poor chemicals with high exposure potential.