Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
BeiGene, Cambridge, MA, USA.
Nat Commun. 2022 Jul 27;13(1):4349. doi: 10.1038/s41467-022-32033-5.
Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. In this study, we construct a multi-domain dataset from a total of 4638 patients in the registrational trials of 16 FDA-approved SMKIs and employ a machine-learning model to examine the relationships between kinase targets and adverse events (AEs). Internal and external (datasets from two independent SMKIs) validations have been conducted to verify the usefulness of the established model. We systematically evaluate the potential associations between 442 kinases with 2145 AEs and made publicly accessible an interactive web application "Identification of Kinase-Specific Signal" ( https://gongj.shinyapps.io/ml4ki ). The developed model (1) provides a platform for experimentalists to identify and verify undiscovered KI-AE pairs, (2) serves as a precision-medicine tool to mitigate individual patient safety risks by forecasting clinical safety signals and (3) can function as a modern drug development tool to screen and compare SMKI target therapies from the safety perspective.
小分子激酶抑制剂 (SMKIs) 在癌症治疗的加速审批计划下正以很快的速度获得批准。在这项研究中，我们从 16 种经美国食品药品监督管理局批准的 SMKI 注册试验中的 4638 名患者中构建了一个多域数据集，并采用机器学习模型来研究激酶靶标与不良事件 (AE) 之间的关系。已经进行了内部和外部（来自两种独立 SMKI 的数据集）验证，以验证所建立模型的有用性。我们系统地评估了 442 种激酶与 2145 种 AE 之间的潜在关联，并公开了一个交互式网络应用程序“鉴定激酶特异性信号”（https://gongj.shinyapps.io/ml4ki）。开发的模型 (1) 为实验人员提供了一个平台，用于识别和验证未发现的 KI-AE 对，(2) 作为一种精准医疗工具，通过预测临床安全信号来减轻个体患者的安全风险，(3) 还可以作为一种现代药物开发工具，从安全性角度筛选和比较 SMKI 靶向治疗药物。
