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The utility of human ADME data for prioritizing the evaluation of pharmaceuticals in the environment.
Citation:
Berninger, J., C. LaLone, D. Villeneuve, C. Russom, Joe Tietge, AND G. Ankley. The utility of human ADME data for prioritizing the evaluation of pharmaceuticals in the environment. Presented at Society for Environmental Toxicology and Chemistry, November 11 - 15, 2012.
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Description:
To proceed in the investigation of potential effects of pharmaceuticals in the environment, a cohesive data collection strategy is paramount. Given the lack of data for aquatic species, prioritization seems a logical starting point. Several methods have been put forward, for example using predicted persistence, bioaccumulation, and toxicity characteristics or sales data models. While these models are valid, critical prioritization strategies, they are not sufficiently comprehensive. Pharmaceuticals are biologically active, with specific physiological targets, suggesting that models built solely on physical-chemical properties and exposure potential cannot be adequate for prioritization. Given the nature of pharmaceuticals, the overall prioritization should include chronic effects and ADME (absorption, distribution, metabolism, excretion) parameters. The lack of available aquatic species data requires the development of an ADME-based prioritization model using read-across from the extensive mammalian knowledge-base. ADME data for 750 drugs were collected including: apparent volume of distribution, clearance rate, half life, protein binding, and therapeutic dose. For this initial prioritization, a probabilistic model was applied to data from each parameter, and subsequently divided into categories based on the each 10th centile. Each centile group was given a corresponding score of 1 to 10 (<10th = 1, 10th 20th centile = 2 … <90th centile =10). Higher scores were based on the likelihood of increased absorption and distribution, or decreased metabolism or elimination. Scores were tallied for each individual drug and summed across all parameters. The resulting prioritized list looks quite different (with some overlap) from lists created from previous prioritization exercises. The unique pharmacokinetic nature of this list provides a basis for the development of testable hypotheses. This ADME-based list has been used to identify potential additions to analy