Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States.
J Biomed Inform. 2024 May;153:104639. doi: 10.1016/j.jbi.2024.104639. Epub 2024 Apr 6.
Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation.
We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals.
Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals.
The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.
尽管药物代谢动力学（PK）药物相互作用（DDI）的机制已有充分记录，但将这些知识与 DDI 的临床证据联系起来仍然具有挑战性，尤其是对于严重药物不良反应（SADR）。虽然利用 FDA 不良事件报告系统（FAERS）数据库和不成比例分析倾向于检测到大量的 DDI 信号，但这种丰富性使得进一步的调查变得复杂，例如通过临床试验进行验证。我们的研究提出了一个框架，以有效地优先考虑这些信号，并使用多源电子健康记录（EHR）评估它们的可靠性，以确定进一步调查的候选者。
我们分析了 2004 年 1 月至 2023 年 3 月的 FAERS 数据，采用了四种已建立的不成比例方法：比例报告比（PRR）、报告比值比（ROR）、多项伽马泊松收缩器（MGPS）和贝叶斯置信传播神经网络（BCPNN）。基于这些模型，我们开发了四个排名模型来优先考虑 DDI-SADR 信号，并与 DrugBank 交叉引用信号。为了验证排名最高的信号，我们使用了范德比尔特大学医学中心和全美国人研究计划的纵向 EHR。通过计算 EHR 确认的排名最高的信号数量并计算这些确认信号的平均排名来评估每个模型的性能。
在所有四种不成比例方法确定的 189 个 DDI-SADR 信号中，只有两个在 DrugBank 数据库中有记录。通过优先考虑每个不成比例方法和我们的四个排名模型确定的前 20 个信号，选择了 58 个独特的 DDI-SADR 信号进行 EHR 验证。其中，有五个信号得到了确认。整合了 MGPS 和 BCPNN 的排名模型通过为五个 EHR 确认信号赋予最高优先级，表现出优越的性能。
将不成比例分析与通过多源 EHR 验证的排名模型相结合，为药物警戒提供了一种开创性的方法。我们的研究证实了五个以前未在 DrugBank 数据库中记录的重要 DDI-SADR，突出了先进数据分析技术在识别不良反应方面的重要作用。
