Abstract：
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.
摘要：
通过系统药理学数据的不断增加，转化方法可以使上市后的药物安全性监测受益。这里,我们提出了一个新的贝叶斯框架,用于识别药物-药物相互作用(DDI)信号和区分单个药物和药物组合信号.该框架与用于自动生物合理性评估的系统药理学方法相结合。综合统计和生物学证据，我们的方法实现了16.5％的改善（AUC：从0.620到0.722）与药物-目标-不良事件关联，16.0%（AUC：从0.580到0.673）与药物酶，和15.0%（AUC：从0.568到0.653）与药物转运蛋白信息。应用该方法检测FDA不良事件报告系统（FAERS）中QT延长和横纹肌溶解的潜在DDI信号，我们强调了系统药理学在药物警戒中增强统计信号检测的重要性。我们的研究展示了在具有挑战性的上市后DDI监测的背景下，数据驱动的生物合理性评估的前景。
