Literature DB >> 11294971

Applications of mechanistic data in risk assessment: the past, present, and future.

L T Haber1, A Maier, Q Zhao, J S Dollarhide, R E Savage, M L Dourson.   

Abstract

Mechanistic data, when available, have long been considered in risk assessment, such as in the development of the nitrate RfD based on effects in a sensitive group (infants). Recent advances in biology and risk assessment methods have led to a tremendous increase in the use of mechanistic data in risk assessment. Toxicokinetic data can improve extrapolation from animals to humans and characterization of human variability. This is done by the development of improved tissue dosimetry, by the use of uncertainty factors based on chemical-specific data, and in the development of physiologically based pharmacokinetic (PBPK) models. The development of the boron RfD illustrates the use of chemical-specific data in the improved choice of uncertainty factors. The draft cancer guidelines of the U.S. Environmental Protection Agency emphasize the use of mode of action data. The first choice under the guidelines is to use a chemical-specific, biologically based dose-response (BBDR) model. In the absence of a BBDR model, mode of action data are used to determine whether low-dose extrapolation is done using a linear or nonlinear (margin of exposure) approach. Considerations involved in evaluating a hypothesized mode of action are illustrated using 1,3-dichloropropene, and use of a BBDR model is illustrated using formaldehyde. Recent developments in molecular biology, including transgenic animals, microarrays, and the characterization of genetic polymorphisms, have significant potential for improving risk assessments, although further methods development is needed. Overall, use of mechanistic data has significant potential for reducing the uncertainty in assessments, while at the same time highlighting the areas of uncertainty.

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Year:  2001        PMID: 11294971     DOI: 10.1093/toxsci/61.1.32

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


  3 in total

Review 1.  Whole body pharmacokinetic models.

Authors:  Ivan Nestorov
Journal:  Clin Pharmacokinet       Date:  2003       Impact factor: 6.447

2.  Evolutionary concepts can benefit both fundamental research and applied research in toxicology (A comment on Brady et al. 2017).

Authors:  Mark E Hahn
Journal:  Evol Appl       Date:  2018-11-05       Impact factor: 5.183

Review 3.  In silico toxicology: computational methods for the prediction of chemical toxicity.

Authors:  Arwa B Raies; Vladimir B Bajic
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2016-01-06
  3 in total

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