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Informing Mechanistic Toxicology with Computational Molecular Models
Citation:
GOLDSMITH, M., S. PETERSON, D. CHANG, T. R. Transue, R. TORNERO-VELEZ, YU-MEI TAN, AND C. C. DARY. Informing Mechanistic Toxicology with Computational Molecular Models. Chapter 7, B Reisfeld, A Mayeno (ed.), Computational Toxicology - Volume I, Methods in Molecular Biology. Springer Science + Business Media, New York, NY, 929(2012):139-165, (2012).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
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.
URLs/Downloads:
Informing Mechanistic Toxicology with Computational Molecular Models (PDF, NA pp, 1046 KB, about PDF)Computational Toxicology Methods in Molecular Biology
