Department of Life Sciences, College of Health, Medicine, and Life Sciences, Brunel University London, London, UB8 3PH, UK.
AstraZeneca, Global Environment, Alderley Park, Macclesfield, Cheshire SK10 4TF, UK.
Environ Int. 2021 Jan;146:106222. doi: 10.1016/j.envint.2020.106222. Epub 2020 Nov 3.
The presence of non-steroidal anti-inflammatory drugs (NSAIDs) in the aquatic environment has raised concern that chronic exposure to these compounds may cause adverse effects in wild fish populations. This potential scenario has led some stakeholders to advocate a stricter regulation of NSAIDs, especially diclofenac. Considering their global clinical importance for the management of pain and inflammation, any regulation that may affect patient access to NSAIDs will have considerable implications for public health. The current environmental risk assessment of NSAIDs is driven by the results of a limited number of standard toxicity tests and does not take into account mechanistic and pharmacological considerations. Here we present a pharmacology-informed framework that enables the prediction of the risk posed to fish by 25 different NSAIDs and their dynamic mixtures. Using network pharmacology approaches, we demonstrated that these 25 NSAIDs display a significant mechanistic promiscuity that could enhance the risk of target-mediated mixture effects near environmentally relevant concentrations. Integrating NSAIDs pharmacokinetic and pharmacodynamic features, we provide highly specific predictions of the adverse phenotypes associated with exposure to NSAIDs, and we developed a visual multi-scale model to guide the interpretation of the toxicological relevance of any given set of NSAIDs exposure data. Our analysis demonstrated a non-negligible risk posed to fish by NSAID mixtures in situations of high drug use and low dilution of waste-water treatment plant effluents. We anticipate that this predictive framework will support the future regulatory environmental risk assessment of NSAIDs and increase the effectiveness of ecopharmacovigilance strategies. Moreover, it can facilitate the prediction of the toxicological risk posed by mixtures via the implementation of mechanistic considerations and could be readily extended to other classes of chemicals.
在水生环境中存在非甾体抗炎药(NSAIDs),这引起了人们的关注,即长期接触这些化合物可能会对野生鱼类种群造成不良影响。这种潜在情况导致一些利益相关者主张对 NSAIDs(尤其是双氯芬酸)实施更严格的监管。考虑到它们在全球范围内用于治疗疼痛和炎症的临床重要性,任何可能影响患者获得 NSAIDs 的监管措施都将对公共卫生产生重大影响。目前对 NSAIDs 的环境风险评估是由少数标准毒性测试的结果驱动的,并没有考虑到机制和药理学方面的考虑。在这里,我们提出了一个药理学信息框架,该框架可以预测 25 种不同的 NSAIDs 及其动态混合物对鱼类构成的风险。我们使用网络药理学方法证明,这 25 种 NSAIDs 表现出显著的机制混杂性,这可能会增加在环境相关浓度下靶介导的混合物效应的风险。通过整合 NSAIDs 的药代动力学和药效学特征,我们对与接触 NSAIDs 相关的不良表型进行了高度特异性预测,并开发了一个可视化多尺度模型,以指导解释任何给定 NSAIDs 暴露数据集的毒理学相关性。我们的分析表明,在高药物使用和低废水处理厂废水稀释的情况下,NSAID 混合物对鱼类构成了不可忽视的风险。我们预计,这种预测框架将支持 NSAIDs 的未来监管环境风险评估,并提高生态药理学监测策略的有效性。此外,它可以通过实施机制考虑来促进对混合物造成的毒理学风险的预测,并且可以很容易地扩展到其他类别的化学物质。