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.

水生环境中存在的非甾体抗炎药 (NSAID) 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利影响。这种潜在的情况导致一些利益相关者主张对非甾体抗炎药（尤其是双氯芬酸）进行更严格的监管。考虑到非甾体抗炎药对于治疗疼痛和炎症的全球临床重要性，任何可能影响患者获得非甾体抗炎药的法规都将对公共健康产生相当大的影响。当前非甾体抗炎药的环境风险评估是由有限数量的标准毒性测试结果驱动的，没有考虑机制和药理学因素。在这里，我们提出了一个基于药理学的框架，可以预测 25 种不同的 NSAID 及其动态混合物对鱼类造成的风险。使用网络药理学方法，我们证明这 25 种 NSAID 表现出显着的机制混杂性，可能会增加接近环境相关浓度的靶标介导的混合物效应的风险。整合非甾体抗炎药的药代动力学和药效学特征，我们提供了与非甾体抗炎药暴露相关的不良表型的高度特异性预测，并且我们开发了一个视觉多尺度模型来指导解释任何给定的非甾体抗炎药暴露数据集的毒理学相关性。我们的分析表明，在大量使用药物和废水处理厂废水稀释程度较低的情况下，非甾体抗炎药混合物对鱼类造成不可忽视的风险。我们预计该预测框架将支持未来非甾体抗炎药的监管环境风险评估，并提高生态药物警戒策略的有效性。 此外，它可以通过实施机械考虑来促进混合物所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品。