A Statistical Network Pharmacology Approach for Early Detection of Adverse Drug Events

药物不良事件早期发现的统计网络药理学方法 项目摘要 在上市后阶段及早发现不良药物事件(ADE)对于保护公众至关重要 从严重的发病率和死亡率。该项目的广泛、长期目标是开发工具和 使科学家能够更早、更可靠地发现ADE的技术。上市后自发报告 药物不良反应系统(SRS)是药物警戒和一系列药物安全信号检测的基石 方法在提供药物安全性见解方面起着重要作用。然而，现有的方法被开发为 在SRS中生成具有足够报告的药物的安全信号，但可以使用的方法很少 对于新近批准的药物，在SRS中几乎没有安全报告。此外，很少有方法来表示信号 在严格的假设检验框架下的检测问题，并且没有方法利用药物标签和药物 属性信息。这个项目的目标是开发新的统计学习方法来解决这些问题 及早以可行的方式挑战和发现ADE： 一、开发综合标签传播框架，基于多种药物对药物安全信号进行重新排序 用于ADE早期检测的相似性网络。我们假设新药的ADE可以是 通过整合多个药物相似性网络，及时检测到。我们将计算原始药物 通过常用信号检测算法获得安全信号。然后，我们将构建基于药物相似度的网络 关于多个数据来源(例如，化学结构、目标、指示)。最后，我们将生产增强型药物 通过在多个药物相似网络上传播原始信号来实现安全信号。建议的方法 通过多个药物相似网络丰富SRS，缓解新批准的病例不足的问题 并为及早发现不良反应药物铺平道路。 开发一种贝叶斯假设检验方法，便于早期发现ADE，同时控制 假阳性率。我们假设所提出的方法增加了检测ADE的能力 与频率法相比，特别是在样本量小的情况下(即，在 新药的批准)。此外，我们假设所提出的方法能够控制误报 费率。具体地说，新药的ADE风险的先验分布可以通过使用现有药物的风险来估计， 新药和现有药物之间的相似度分数，以及药物的标签信息。随后， 可以利用先验分布和观测数据来导出零假设的后验概率， 应用于及时检测ADE。

A Statistical Network Pharmacology Approach for Early Detection of Adverse Drug Events Project Summary Early detection of adverse drug events (ADEs) in the post market phase is essential for protecting the public from significant morbidity and mortality. The broad, long-term objectives of this project are to develop tools and techniques that enable scientists to discover ADEs earlier and more reliably. Post-market spontaneous reporting system (SRS) of ADEs serves as a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods are developed to generate safety signals for drugs with enough reports in SRS, but few methods can be used to generate signals for newly approved drugs with few or even no safety reports in SRS. Also, few methods formulate the signal detection problem under a rigorous hypothesis test framework, and no method exploits drug label and drug property information. The goal of this project is to develop novel statistical learning methods to tackle those challenges and detect ADEs in an early and actionable manner: I. Develop an integrative label propagation framework to re-rank drug safety signals based on multiple drug similarity networks for early detection of ADEs. We hypothesize that ADEs of newly approved drugs can be detected in a timely fashion by incorporating multiple drug similarity networks. We will compute original drug safety signals via common signal detection algorithms. Then, we will construct drug similarity networks based on multiple data sources (e.g., chemical structures, targets, indications). Finally, we will generate enhanced drug safety signals by propagating original signals on multiple drug similarity networks. The proposed method enriches SRS with multiple drug similarity networks, alleviating issues of insufficient cases for newly approved drugs and paving the way for early detection of ADEs. II. Develop a Bayesian hypothesis testing approach which facilitates early detection of ADEs, while controlling the false positive rate. We hypothesize that the proposed approach has increased power to detect ADEs comparing with frequentist approaches, especially when the sample size is small (i.e., in a short period after a new drug’s approval). Additionally, we hypothesize that the proposed approach is able to control the false positive rate. Specifically, prior distribution of the new drug’s ADE risk can be estimated by using the existing drugs’ risks, similarity scores between the new drug and the existing drugs, and drugs’ label information. Subsequently, the prior distribution and the observed data can be utilized to derive the posterior probability of the null hypothesis, which shall be used to detect ADE in a timely fashion.