Literature DB >> 20970440

Application of toxicogenomics in hepatic systems toxicology for risk assessment: acetaminophen as a case study.

Anne S Kienhuis1, Jos G M Bessems, Jeroen L A Pennings, Marja Driessen, Mirjam Luijten, Joost H M van Delft, Ad A C M Peijnenburg, Leo T M van der Ven.   

Abstract

Hepatic systems toxicology is the integrative analysis of toxicogenomic technologies, e.g., transcriptomics, proteomics, and metabolomics, in combination with traditional toxicology measures to improve the understanding of mechanisms of hepatotoxic action. Hepatic toxicology studies that have employed toxicogenomic technologies to date have already provided a proof of principle for the value of hepatic systems toxicology in hazard identification. In the present review, acetaminophen is used as a model compound to discuss the application of toxicogenomics in hepatic systems toxicology for its potential role in the risk assessment process, to progress from hazard identification towards hazard characterization. The toxicogenomics-based parallelogram is used to identify current achievements and limitations of acetaminophen toxicogenomic in vivo and in vitro studies for in vitro-to-in vivo and interspecies comparisons, with the ultimate aim to extrapolate animal studies to humans in vivo. This article provides a model for comparison of more species and more in vitro models enhancing the robustness of common toxicogenomic responses and their relevance to human risk assessment. To progress to quantitative dose-response analysis needed for hazard characterization, in hepatic systems toxicology studies, generation of toxicogenomic data of multiple doses/concentrations and time points is required. Newly developed bioinformatics tools for quantitative analysis of toxicogenomic data can aid in the elucidation of dose-responsive effects. The challenge herein is to assess which toxicogenomic responses are relevant for induction of the apical effect and whether perturbations are sufficient for the induction of downstream events, eventually causing toxicity.
© 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20970440     DOI: 10.1016/j.taap.2010.10.013

Source DB:  PubMed          Journal:  Toxicol Appl Pharmacol        ISSN: 0041-008X            Impact factor:   4.219


  7 in total

1.  Serum proteomic profiling in patients with drug-induced liver injury.

Authors:  L N Bell; R Vuppalanchi; P B Watkins; H L Bonkovsky; J Serrano; R J Fontana; M Wang; J Rochon; N Chalasani
Journal:  Aliment Pharmacol Ther       Date:  2012-03       Impact factor: 8.171

2.  Ecotoxicological risk assessment and seasonal variation of some pharmaceuticals and personal care products in the sewage treatment plant and surface water bodies (lakes).

Authors:  G Archana; Rita Dhodapkar; Anupama Kumar
Journal:  Environ Monit Assess       Date:  2017-08-10       Impact factor: 2.513

Review 3.  Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

Authors:  Yen Sia Low; Alexander Yeugenyevich Sedykh; Ivan Rusyn; Alexander Tropsha
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

4.  Nanoparticle toxicity by the gastrointestinal route: evidence and knowledge gaps.

Authors:  Ingrid L Bergin; Frank A Witzmann
Journal:  Int J Biomed Nanosci Nanotechnol       Date:  2013

5.  Effect of chemical mutagens and carcinogens on gene expression profiles in human TK6 cells.

Authors:  Lode Godderis; Reuben Thomas; Alan E Hubbard; Ali M Tabish; Peter Hoet; Luoping Zhang; Martyn T Smith; Hendrik Veulemans; Cliona M McHale
Journal:  PLoS One       Date:  2012-06-18       Impact factor: 3.240

6.  Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure.

Authors:  Julie A Bourdon; Andrew Williams; Byron Kuo; Ivy Moffat; Paul A White; Sabina Halappanavar; Ulla Vogel; Håkan Wallin; Carole L Yauk
Journal:  Toxicology       Date:  2012-11-09       Impact factor: 4.221

7.  Construction of a computable network model for DNA damage, autophagy, cell death, and senescence.

Authors:  Stephan Gebel; Rosemarie B Lichtner; Brian Frushour; Walter K Schlage; Vy Hoang; Marja Talikka; Arnd Hengstermann; Carole Mathis; Emilija Veljkovic; Michael Peck; Manuel C Peitsch; Renee Deehan; Julia Hoeng; Jurjen W Westra
Journal:  Bioinform Biol Insights       Date:  2013-03-07
  7 in total

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