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ARE EXPOSURE PREDICTIONS, USED FOR THE PRIORITISATION OF PHARMACEUTICALS IN THE ENVIRONMENT, FIT FOR PURPOSE?

机译:暴露预测,用于优先考虑环境中的药物,是否适用于目的?

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摘要

Prioritisation methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring towards pharmaceuticals of greatest concern. Risk-based prioritisation approaches, employing models to derive exposure concentrations, are commonly used but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritisation. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York, UK. Predicted environmental concentration (PEC) ranges were estimated based on localised prescription, hydrological data, reported metabolism and wastewater treatment plant (WwTP) removal rates, and were compared to measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WwTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly due to pharmaceutical inputs beyond our modelling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With two exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritisation of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs to the system. This article is protected by copyright. All rights reserved.
机译:优先排序方法通常用于识别对自然环境构成最大风险的药物,并将实验室测试或环境监测的重点放在最受关注的药物上。通常使用基于风险的优先级排序方法,该模型采用模型来得出暴露浓度,但是这些模型的可靠性尚不清楚。本研究评估了通常用于药物优先排序的暴露模型的准确性。在英国约克市的Rivers Foss和Ouse进行了针对95种药品的目标监测。根据本地处方,水文数据,报告的代谢和废水处理厂(WwTP)去除率,估算了预计的环境浓度(PEC)范围,并将其与测得的环境浓度(MEC)进行了比较。对于Rives Foss,使用最高代谢和最低WwTP去除率获得的PEC与MEC相似。相反,在Ouse河中未观察到这种趋势,这可能是由于我们建模以外的药物投入所致。根据MEC或PEC按风险对药品进行排名。除了两个例外(右美沙芬和苯海拉明),基于MEC和PEC的风险等级在福斯河产生了相似的结果。总体而言,这些发现表明,当可获得有关目标系统的可靠且本地的数据并反映出该系统的大多数来源输入时,PEC可能非常适合环境中药物的优先级排序。本文受版权保护。版权所有。

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