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

用于早期检测药物不良事件的统计网络药理学方法

• 批准号: 10185087
• 负责人: Pengyue Zhang
• 金额: $ 32.82万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10185087
• 关键词: Acquired Immunodeficiency Syndrome; Address; Adverse drug event; Adverse event; Algorithms; Bayesian Method; Biological; Case Study; Cause of Death; Cessation of life; Chemical Structure; Chemicals; Clinical Trials; Computer software; Data; Detection; Diabetes Mellitus; Disease; Drug usage; Early Diagnosis; Epidemiology; Event; Goals; Gold; Health; Health Policy; Label; Literature; Lung diseases; Medical Care Costs; Methodology; Methods; Morbidity - disease rate; Network-based; New Drug Approvals; Odds Ratio; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Phase; Play; Probability; Property; Public Health; Reporting; Research; Risk; Role; Safety; Sample Size; Scientist; Series; Signal Transduction; Source; Statistical Methods; Structure; System; Techniques; Testing; Time; United States; base; design; detection method; improved; insight; learning strategy; medication safety; mortality; multiple data sources; neural network; novel; novel therapeutics; open source; operation; patient safety; pharmacovigilance; post-market; statistical learning; tool; user-friendly

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.

基于统计网络药理学的药物不良事件早期检测方法 项目摘要 在上市后阶段早期发现药物不良事件（ADE）对于保护公众至关重要 发病率和死亡率都很高该项目的广泛和长期目标是开发工具， 这些技术使科学家能够更早、更可靠地发现ADE。上市后自发报告 ADE的SRS系统是药物警戒和一系列药物安全性信号检测的基石 方法在提供药物安全见解方面发挥重要作用。然而，现有的方法被开发为 SRS中有足够报告的药物生成安全性信号，但生成信号的方法很少 对于SRS中很少甚至没有安全性报告的新批准药物。此外，很少有方法将信号 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 财产信息。该项目的目标是开发新的统计学习方法来解决这些问题 以早期可行的方式挑战和检测ADE： I.开发一个综合标签传播框架，以基于多种药物对药物安全性信号进行重新排序 相似性网络用于早期检测ADE。我们假设，新批准的药物的ADE可能是 通过整合多个药物相似性网络及时检测。我们将计算原始药物 安全信号通过共同的信号检测算法。然后，我们将构建药物相似性网络， 在多个数据源上（例如，化学结构、目标、指示）。最后，我们将产生增强的药物 通过在多个药物相似性网络上传播原始信号来获得安全信号。该方法 通过多种药物相似性网络丰富SRS，缓解新批准病例不足的问题 为早期发现ADE铺平道路。 二.开发贝叶斯假设检验方法，促进ADE的早期检测，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 与频率论方法相比，特别是当样本大小很小时（即，在一个短时间内， 新药批准）。此外，我们假设所提出的方法能够控制假阳性 率具体地说，新药ADE风险的先验分布可以利用现有药物的风险来估计， 新药与现有药物之间的相似性分数以及药物的标签信息。随后 先验分布和观测数据可用于导出零假设的后验概率， 用于及时检测ADE。