4.5 Article

A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP FOR PREDICTING METABOLIC BIOTRANSFORMATION RATES FOR ORGANIC CHEMICALS IN FISH

Journal

ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY
Volume 28, Issue 6, Pages 1168-1177

Publisher

WILEY-BLACKWELL
DOI: 10.1897/08-289.1

Keywords

Fish; Biotransformation; Bioaccumulation; Xenobiotic metabolism; Quantitative structure-activity relationship

Funding

  1. Natural Sciences and Engineering Research Council of Canada
  2. Environment Canada
  3. Health Canada
  4. consortium of companies
  5. International Life Sciences Institute-Health and Environmental Sciences Institute
  6. U.S. Environmental Protection Agency (Office of Pollution Prevention and Toxics)

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An evaluated database of whole body in vivo biotransformation rate estimates in fish was used to develop a model for predicting the primary biotransformation half-lives of organic chemicals. The estimated biotransformation rates were converted to half-lives and divided into a model development set (n = 421) and an external validation set (n = 211) to test the model. The model uses molecular substructures similar to those of other biodegradation models. The biotransformation half-life predictions were calculated based on multiple linear regressions of development set data against counts of 57 molecular substructures, the octanol-water partition coefficient, and molar mass. The coefficient of determination (r(2)) for the development set was 0.82, the cross-validation (leave-one-out coefficient of determination, q(2)) was 0.75, and the mean absolute error (MAE) was 0.38 log units (factor of 2.4). Results for the external validation of the model using an independent test set were r(2) = 0.73 and MAE = 0.45 log units (factor of 2.8). For the development set, 68 and 95% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. For the test (or validation) set, 63 and 90% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. Reasons for discrepancies between model predictions and expected values are discussed and recommendations are made for improving the model. This model can predict biotransformation rate constants from chemical structure for screening level bioaccumulation hazard assessments, exposure and risk assessments, comparisons with other in vivo and in vitro estimates, and as a contribution to testing strategies that reduce animal usage.

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