Computational toxicology
Contents
Introduction (Computational toxicology)
Background
Gene expression array used in computational toxicology. (Source: EPA) Computational Toxicology is an emerging and innovative field comprised of disciplines and methodological approaches designed to understand—and safeguard—public health and the environment from harmful effects that may be caused by exposure to pollutants in the air, water, soil, and food. Protecting human health and the environment carries with it the challenge of assessing the risk that is posed by tens of thousands of chemicals. The large number of chemicals that need evaluation and the various legal statutes that regulate chemicals have traditionally made it impossible for the science to evaluate every chemical with the most rigorous testing strategies. Instead, standard toxicity tests have been limited to only a small number of chemicals. Today, however, the young field of computational biology offers the possibility that, with advances in computational biology's subdisciplines (e.g., genomics, proteomics, and metabonomics), scientists may have the ability to develop a more detailed understanding of the risks posed by a much larger number of chemicals. The application of the tools of computational biology to assess the risk chemicals pose to human health and the environment is termed Computational Toxicology.
The U.S. Environmental Protection Agency and Computational Toxicology
The U.S. Environmental Protection Agency (EPA) defines Computational Toxicology as the application of mathematical and computer models to predict adverse effects and to better understand the single or multiple mechanisms through which a given chemical induces harm.
Three strategic objectives of EPA's computational toxicology initiative are to:
- improve understanding of the linkages in the continuum between the source of a chemical in the environment and adverse outcomes;
- provide predictive models for screening and testing; and
- improve quantitative risk assessment.
Computational Disciplines
Computational toxicology includes several computational disciplines including:
- Computational chemistry, which refers to physical-chemical mathematical modeling at the molecular level and includes such topics as quantum chemistry, force fields, molecular mechanics, molecular simulations, molecular modeling, molecular design, and cheminformatics;
- Computational biology or bioinformatics, which refers to development of molecular biology databases and the analysis of the data; and
- Systems biology, which refers to the application of mathematical modeling and reasoning to the understanding of biological systems and the explanation of biological phenomena.
"Omic" Disciplines
The development of "omic" technologies has evolved into three scientific disciplines:
- genomics which is defined as the study of genes and their function;
- proteomics which is defined as the study of the full set of proteins encoded by a genome; and
- metabonomics which is defined as the study of the total metabolite pool.
Several recent technological advances now make it possible to develop molecular profiles using genomic, proteomic, and metabolomic methods in order to identify the effects that chemicals may have on living organisms or the environment. Although the technology continues to change and improve, conducting these types of analyses is no longer a question of capability. The use of "omic" technology to study toxicological questions is called toxicogenomics.
EPA has prepared a Framework for a Computational Toxicology Research Program.
Further Reading
- A Framework for a Computational Toxicology Research Program, U.S. EPA
- About Computational Toxicology Research, U.S. EPA
- Application of Toxicogenomics to Cross-Species Extrapolation: A Report of a Workshop, National Academies Press
- Intellectual Property Concerns for Toxicogenomics, National Academies
- National Research Council's Committee on Emerging Issues and Data on Environmental Contaminants, National Research Council
- Toxicity Testing for Assessment of Environmental Agents: Interim Report, National Academies Press
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