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Informing Mechanistic Toxicology with Computational Molecular Models

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Computational Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 929))

Abstract

Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo efforts. From a molecular biophysical ansatz, we describe how 3D molecular modeling methods used to numerically evaluate the classical pair-wise potential at the chemical/biological interface can inform mechanism of action and the dose–response paradigm of modern toxicology. With an emphasis on molecular docking, 3D-QSAR and pharmacophore/toxicophore approaches, we demonstrate how these methods can be integrated with chemoinformatic and toxicogenomic efforts into a tiered computational toxicology workflow. We describe generalized protocols in which 3D computational molecular modeling is used to enhance our ability to predict and model the most relevant toxicokinetic, metabolic, and molecular toxicological endpoints, thereby accelerating the computational toxicology-driven basis of modern risk assessment while providing a starting point for rational sustainable molecular design.

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Notes

  1. 1.

    Historically molecular modeling methods, stemming from roots in theoretical and computational chemistry, are composed of an ensemble of developed and thoroughly vetted computational approaches used to investigate molecular-level processes and phenomena including but not limited to molecular structure, chemical catalysis, geochemistry, interfacial chemistry, nanotechnology, conformational analysis, stereoselectivity, enzyme biochemistry, chemical reaction dynamics, solvation, molecular aggregation, and molecular design.

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Acknowledgments

Michael-Rock Goldsmith would like to thank James Rabinowitz and Stephen Little (from the US-EPA’s National Center for Computational Toxicology) for providing mentorship and assistance during his postdoctoral research, and providing the environment to explore molecular docking in the context of toxicology while providing insight and valuable discussion in the development of the in-house in silico chemical genomics initiative at the US-EPA.

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Authors and Affiliations

  1. National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC, USA

    Michael R. Goldsmith, Shane D. Peterson & Daniel T. Chang

  2. Lockheed Martin Information Technology, Research Triangle Park, NC, USA

    Thomas R. Transue

  3. National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

    Rogelio Tornero-Velez & Yu-Mei Tan

  4. National Exposure Research laboratory, US Environmental Protection Agency, Research Triangle Park, NC, USA

    Curtis C. Dary

Authors
  1. Michael R. Goldsmith
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  2. Shane D. Peterson
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  3. Daniel T. Chang
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  4. Thomas R. Transue
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  5. Rogelio Tornero-Velez
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  6. Yu-Mei Tan
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  7. Curtis C. Dary
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Correspondence to Michael R. Goldsmith .

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Editors and Affiliations

  1. School of Biomedical Engineering, Chemical & Biological Engineering, Colorado State University, Campus Delivery 1370, Fort Collins, 80523-1370, Colorado, USA

    Brad Reisfeld

  2. , Chemical & Biological Engineering, Colorado State University, Campus Delivery 1370, Fort Collins, 80523-1370, Colorado, USA

    Arthur N. Mayeno

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Goldsmith, M.R. et al. (2012). Informing Mechanistic Toxicology with Computational Molecular Models. In: Reisfeld, B., Mayeno, A. (eds) Computational Toxicology. Methods in Molecular Biology, vol 929. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-050-2_7

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  • DOI: https://doi.org/10.1007/978-1-62703-050-2_7

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-049-6

  • Online ISBN: 978-1-62703-050-2

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