Tepotinib 被批准用于治疗携带 MET 外显子 14 跳跃改变的非小细胞肺癌患者。虽然水肿是最常见的不良事件 (AE)，也是包括替泊替尼在内的 MET 抑制剂的已知类别效应，但对于导致其发生的因素仍知之甚少。在此，我们应用基于机器学习（ML）的方法来预测接受替泊替尼治疗的患者发生水肿的可能性，并确定随着时间的推移影响其发展的因素。五项 I/II 期研究中 612 名接受 tepotinib 治疗的患者的数据使用两种 ML 算法（随机森林和梯度提升树）进行建模，以预测水肿 AE 的发生率和严重程度。应用概率校准来对水肿 AE 的可能性进行实际估计。最佳模型根据后续数据和训练时未使用的临床研究数据进行了测试。结果显示，在所有测试设置中均表现出色，使用最相关的协变量重新训练模型时，F1 分数高达 0.961。使用机器学习解释方法将血清白蛋白确定为信息最丰富的纵向协变量，并且年龄越大，水肿越严重的可能性越高。开发的方法框架允许使用机器学习算法来分析临床安全数据并通过各种协变量工程方法利用纵向信息。概率校准可确保准确估计 AE 发生的可能性，而可解释性工具可以识别有助于模型预测的因素，从而支持人群和个体患者水平的解释。© 2024 作者。 《临床和转化科学》由 Wiley periodicals LLC 代表美国临床药理学和治疗学会出版。

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is still limited understanding about the factors contributing to its occurrence. Herein, we apply machine learning (ML)-based approaches to predict the likelihood of occurrence of edema in patients undergoing tepotinib treatment, and to identify factors influencing its development over time. Data from 612 patients receiving tepotinib in five Phase I/II studies were modeled with two ML algorithms, Random Forest, and Gradient Boosting Trees, to predict edema AE incidence and severity. Probability calibration was applied to give a realistic estimation of the likelihood of edema AE. Best model was tested on follow-up data and on data from clinical studies unused while training. Results showed high performances across all the tested settings, with F1 scores up to 0.961 when retraining the model with the most relevant covariates. The use of ML explainability methods identified serum albumin as the most informative longitudinal covariate, and higher age as associated with higher probabilities of more severe edema. The developed methodological framework enables the use of ML algorithms for analyzing clinical safety data and exploiting longitudinal information through various covariate engineering approaches. Probability calibration ensures the accurate estimation of the likelihood of the AE occurrence, while explainability tools can identify factors contributing to model predictions, hence supporting population and individual patient-level interpretation.© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.