Literature DB >> 18065772

Computational toxicology--a state of the science mini review.

Robert J Kavlock1, Gerald Ankley, Jerry Blancato, Michael Breen, Rory Conolly, David Dix, Keith Houck, Elaine Hubal, Richard Judson, James Rabinowitz, Ann Richard, R Woodrow Setzer, Imran Shah, Daniel Villeneuve, Eric Weber.   

Abstract

Advances in computer sciences and hardware combined with equally significant developments in molecular biology and chemistry are providing toxicology with a powerful new tool box. This tool box of computational models promises to increase the efficiency and the effectiveness by which the hazards and risks of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types of biological interactions that can be evaluated. In addition, knowledge of toxicity pathways gathered within the tool box will be directly applicable to the study of the biological responses across a range of dose levels, including those more likely to be representative of exposures to the human population. Progress in this field will facilitate the transformative shift called for in the recent report on toxicology in the 21st century by the National Research Council. This review surveys the state of the art in many areas of computational toxicology and points to several hurdles that will be important to overcome as the field moves forward. Proof-of-concept studies need to clearly demonstrate the additional predictive power gained from these tools. More researchers need to become comfortable working with both the data generating tools and the computational modeling capabilities, and regulatory authorities must show a willingness to the embrace new approaches as they gain scientific acceptance. The next few years should witness the early fruits of these efforts, but as the National Research Council indicates, the paradigm shift will take a long term investment and commitment to reach full potential.

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Year:  2007        PMID: 18065772     DOI: 10.1093/toxsci/kfm297

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  21 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

2.  A multiparametric organ toxicity predictor for drug discovery.

Authors:  Chirag N Patel; Sivakumar Prasanth Kumar; Rakesh M Rawal; Daxesh P Patel; Frank J Gonzalez; Himanshu A Pandya
Journal:  Toxicol Mech Methods       Date:  2019-10-29       Impact factor: 2.987

3.  Widespread Dysregulation of Long Noncoding Genes Associated With Fatty Acid Metabolism, Cell Division, and Immune Response Gene Networks in Xenobiotic-exposed Rat Liver.

Authors:  Kritika Karri; David J Waxman
Journal:  Toxicol Sci       Date:  2020-04-01       Impact factor: 4.849

Review 4.  From Classical Toxicology to Tox21: Some Critical Conceptual and Technological Advances in the Molecular Understanding of the Toxic Response Beginning From the Last Quarter of the 20th Century.

Authors:  Supratim Choudhuri; Geoffrey W Patton; Ronald F Chanderbhan; Antonia Mattia; Curtis D Klaassen
Journal:  Toxicol Sci       Date:  2018-01-01       Impact factor: 4.849

5.  lapdMouse: a data archive for advancing computational models of inhaled aerosol dosimetry.

Authors:  Guilherme J M Garcia
Journal:  J Appl Physiol (1985)       Date:  2020-01-23

6.  From raw materials to validated system: the construction of a genomic library and microarray to interpret systemic perturbations in Northern bobwhite.

Authors:  Arun Rawat; Kurt A Gust; Youping Deng; Natàlia Garcia-Reyero; Michael J Quinn; Mark S Johnson; Karl J Indest; Mohamed O Elasri; Edward J Perkins
Journal:  Physiol Genomics       Date:  2010-04-20       Impact factor: 3.107

Review 7.  Paradigm shift in toxicity testing and modeling.

Authors:  Hongmao Sun; Menghang Xia; Christopher P Austin; Ruili Huang
Journal:  AAPS J       Date:  2012-04-20       Impact factor: 4.009

8.  Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals.

Authors:  Susan A Csiszar; David E Meyer; Kathie L Dionisio; Peter Egeghy; Kristin K Isaacs; Paul S Price; Kelly A Scanlon; Yu-Mei Tan; Kent Thomas; Daniel Vallero; Jane C Bare
Journal:  Environ Sci Technol       Date:  2016-10-18       Impact factor: 9.028

Review 9.  Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science.

Authors:  M Ariel Geer Wallace; Tzipporah M Kormos; Joachim D Pleil
Journal:  J Toxicol Environ Health B Crit Rev       Date:  2016-10-19       Impact factor: 6.393

10.  Fathead minnow steroidogenesis: in silico analyses reveals tradeoffs between nominal target efficacy and robustness to cross-talk.

Authors:  Jason E Shoemaker; Kalyan Gayen; Natàlia Garcia-Reyero; Edward J Perkins; Daniel L Villeneuve; Li Liu; Francis J Doyle
Journal:  BMC Syst Biol       Date:  2010-06-28
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