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.

在水生环境中非甾体类抗炎药（NSAIDs）的存在引起了人们的担忧，即长期接触这些化合物可能会对野生鱼类种群造成不利影响。这种潜在的情况导致一些利益相关者提倡对非甾体抗炎药，尤其是双氯芬酸进行更严格的监管。考虑到它们在疼痛和炎症控制方面的全球临床重要性，任何可能影响患者获得NSAID的途径的法规都将对公共健康产生重大影响。当前对非甾体抗炎药的环境风险评估是由有限数量的标准毒性试验的结果决定的，并且没有考虑到机理和药理学方面的考虑。在这里，我们介绍了一种药理学知识框架，可以预测25种不同的NSAID及其动态混合物对鱼类构成的风险。使用网络药理学方法，我们证明了这25种非甾体抗炎药具有明显的机械混杂性，可以在环境相关浓度附近提高靶标介导的混合物效应的风险。整合了NSAIDs的药代动力学和药效学特征，我们提供了与暴露于NSAIDs相关的不良表型的高度特异性预测，并且我们开发了可视化多尺度模型来指导对任何给定的NSAIDs暴露数据的毒理学相关性的解释。我们的分析表明，在毒品大量使用和废水处理厂废水稀释率低的情况下，非甾体抗炎药混合物对鱼类构成的风险不可忽略。我们预计，该预测框架将支持NSAID的未来监管环境风险评估，并提高生态药物警戒策略的有效性。此外，它可以通过实施机理考虑因素来促进混合物所构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品。