Translational approaches can benefit post‐marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug–drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug‐target‐adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data‐driven biological plausibility assessment in the context of challenging post‐marketing DDI surveillance.

中文翻译：

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利用信号检测和生物学合理性方面识别自发报告中的药物相互作用

通过不断增加系统药理学数据的可用性，转化方法可以有利于上市后药物安全监测。在这里，我们提出了一种新颖的贝叶斯框架，用于识别药物相互作用（DDI）信号并区分单个药物和药物组合信号。该框架与系统药理学方法相结合，用于自动生物合理性评估。综合统计和生物学证据，我们的方法在药物-靶标-不良事件关联方面实现了 16.5% 的改善（AUC：从 0.620 到 0.722），在药物酶方面实现了 16.0%（AUC：从 0.580 到 0.673）的改善，在药物酶方面实现了 15.0%（AUC：从 0.580 到 0.673）的改善。从 0.568 到 0.653）以及药物转运蛋白信息。应用这种方法来检测 FDA 不良事件报告系统 (FAERS) 内 QT 延长和横纹肌溶解的潜在 DDI 信号，我们强调系统药理学在增强药物警戒中统计信号检测方面的重要性。我们的研究展示了在具有挑战性的上市后 DDI 监测背景下数据驱动的生物合理性评估的前景。