Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Takeda Pharmaceuticals U.S.A., Inc, Cambridge, Massachusetts, USA.
Clin Transl Sci. 2021 Sep;14(5):1864-1874. doi: 10.1111/cts.13035. Epub 2021 May 3.
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.
临床试验效率，定义为促进患者入组，以及减少达到安全性和疗效决策点的时间，是改善治疗开发的关键驱动因素。本研究评估了一种机器学习（ML）方法，以改进旨在治疗精神分裂症中未满足医疗需求的 II 期或概念验证试验。使用来自临床抗精神病药物干预疗效试验（CATIE）的诊断数据来开发一个二进制分类 ML 模型，预测个体患者的反应是“改善”，定义为总阳性和阴性综合征量表（PANSS）评分降低 20%以上，还是“无改善”，定义为治疗反应不足（总 PANSS 评分降低<20%）。随机森林算法在模型对治疗 6 个月后的患者进行分类的能力方面相对于其他基于树的方法表现最佳。尽管模型识别真阳性（衡量模型敏感性的指标）的能力较差（<0.2），但其特异性、真阴性率很高（0.948）。随后，基于第一个模型改编的第二个模型被应用为 ML 方法的概念验证，通过根据基线诊断评分识别预计不会改善的患者来补充试验入组。在三个应用这种筛选方法的虚拟试验中，预计会改善的患者比例从 46%到 48%不等，始终约为 CATIE 反应率 22%的两倍。这些结果表明，ML 在提高临床试验效率方面具有广阔的应用前景，因此 ML 模型值得进一步考虑和开发。
