Wang Dulin, Ling Yaobin, Harris Kristofer, Schulz Paul E, Jiang Xiaoqian, Kim Yejin
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
AMIA Annu Symp Proc. 2025 May 22;2024:1176-1185. eCollection 2024.
Characterizing differential responses to Alzheimer's disease (AD) drugs will provide better insights into personalized treatment strategies. Our study aims to identify heterogeneous treatment effects and pre-treatment features that moderate the treatment effect of Galantamine, Bapineuzumab, and Semagacestat from completed trial data. The causal forest method can capture heterogeneity in treatment responses. We applied causal forest modeling to estimate the treatment effect and identify efficacy moderators in each trial. We found several patient's pretreatment conditions that determined treatment efficacy. For example, in Galantamine trials, whole brain volume (1092.54 vs. 1060.67 ml, P < .001) and right hippocampal volume (2.43e-3 vs. 2.79e-3, P < .001) are significantly different between responsive and non-responsive subgroups. Overall, our implementation of causal forests in AD clinical trials reveals the heterogeneous treatment effects and different moderators for AD drug responses, highlighting promising personalized treatment based on patient-specific characteristics in AD research and drug development.
表征对阿尔茨海默病（AD）药物的差异反应将为个性化治疗策略提供更好的见解。我们的研究旨在从已完成的试验数据中识别异质性治疗效果以及调节加兰他敏、巴匹纽单抗和塞马西坦治疗效果的治疗前特征。因果森林方法可以捕捉治疗反应中的异质性。我们应用因果森林模型来估计治疗效果，并在每个试验中识别疗效调节因素。我们发现了几个决定治疗效果的患者治疗前状况。例如，在加兰他敏试验中，有反应和无反应亚组之间的全脑体积（1092.54对1060.67毫升，P <.001）和右侧海马体积（2.43e - 3对2.79e - 3，P <.001）存在显著差异。总体而言，我们在AD临床试验中实施因果森林揭示了AD药物反应的异质性治疗效果和不同的调节因素，突出了基于AD研究和药物开发中患者特定特征的有前景的个性化治疗。
