Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning

协调阿尔茨海默病的多项临床试验，通过联合反事实学习研究对治疗的差异反应

• 批准号: 10714797
• 负责人: Xiaoqian Jiang
• 金额: $ 67.93万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714797
• 关键词: Address; Affect; Agreement; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Antihypertensive Agents; Atrophic; Characteristics; Chronic; Clinical Data; Clinical Trials; Cognitive; Data; Data Set; Disease Progression; Ensure; Failure; Friends; Future; Galantamine; Gender; Gold; Grain; Hippocampus; Impaired cognition; Individual; Knowledge; Learning; Modality; Modeling; Nerve Degeneration; Obesity; Outcome; Outsourcing; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Phase II Clinical Trials; Phenotype; Plasma; Policies; Population; Prediction of Response to Therapy; Reporting; Research; Risk; Sample Size; Subgroup; Subjects Selections; Training; comorbidity; data access; deep learning; distributed data; drug development; federated learning; forest; individual response; informatics tool; innovation; machine learning model; machine learning prediction; outcome prediction; patient population; patient response; patient subsets; phase III trial; predictive modeling; privacy preservation; prospective; randomized, clinical trials; response; sex; success; therapy development; transmission process; treatment effect; treatment response

Drug development for treating Alzheimer's disease (AD) has been challenging and expensive. Drug failures are very likely due, in large part, to the differential responses of patients to different treatments. Some subsets of patients have treatment moderators and respond differently. Identifying such responsive subsets has been challenging due to limited sample size in one clinical trial or may be beyond the scope of the ad-hoc analyses in individual clinical trials, considering the complexity of AD. Another important subset of patients are rapid progressors, who have faster rates of cognitive decline in a defined period and may respond differently to treatments than other AD patients. Predicting the rapid progressors and their differential responses is very challenging. Machine learning prediction has been no better than random guesses due to volatility of cognitive scores and insufficiency of comprehensive and fine-grained longitudinal clinical data. Pooling patient-level data from multiple clinical trials data may address the above challenges by increasing sample size and obtaining a better coverage/representation of the patient population. However, many clinical trials data are stored in distributed data access servers, and data use agreements often prohibit exporting the patient- level data out of the local servers. We aim to address the challenges via advanced informatics tools using AI/ML models. We will develop privacy-preserving federated models to harmonize local counterfactual effect estimation models into a global model without exchanging patient- level data. Aim 1 focuses on developing a federated subgrouping model based on differential responses. Aim 2 focuses on developing a federated counterfactual regression model using deep learning to predict rapid progressors and their differential responses. Aim 3 focuses on verifying and refining the subgroups prediction using real-world observation in nation-wide consortium data. If successful, this project will contribute to identifying patient subgroups that respond differently, which will result in smaller, less expensive, and more targeted AD clinical trials that expose fewer patients to experimental medications to which they are unlikely to respond.

治疗阿尔茨海默病（AD）的药物开发一直具有挑战性且昂贵。