John Wiley & Sons, Ltd.

Prioritization of pharmaceuticals for potential environmental hazard through leveraging a large scale mammalian pharmacological dataset

0
The potential for pharmaceuticals in the environment to cause adverse ecological effects is of increasing concern. Given the thousands of active pharmaceutical ingredients (APIs) which can enter the aquatic environment through human and/or animal (e.g., livestock) waste, a current challenge in aquatic toxicology is identifying those that pose the greatest risk. Because empirical toxicity information for aquatic species is generally lacking for pharmaceuticals, an important data source for prioritization is that generated during the mammalian drug development process. Applying concepts of species read‐across, mammalian pharmacokinetic data were used to systematically prioritize APIs by estimating their potential to cause adverse biological consequences to aquatic organisms, using fish as an example. Mammalian ADME (absorption, distribution, metabolism, excretion) data (e.g., peak plasma concentration, apparent volume of distribution, clearance rate and half life) were collected and curated, creating the Mammalian Pharmacokinetic Prioritization For Aquatic Species Targeting (MaPPFAST) database representing 1070 APIs. From these data a probabilistic model and scoring system were developed and evaluated. Individual APIs and therapeutic classes were ranked based on clearly defined read‐across assumptions for translating mammalian derived ADME parameters to estimate potential hazard in fish (i.e., greatest predicted hazard associated with lowest mammalian peak plasma concentrations, total clearance and highest volume of distribution, half life). It is anticipated that the MaPPFAST database and the associated API prioritization approach will help guide research and/or inform ecological risk assessment. This article is protected by copyright. All rights reserved

Customer comments

No comments were found for Prioritization of pharmaceuticals for potential environmental hazard through leveraging a large scale mammalian pharmacological dataset. Be the first to comment!