Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods.

The traditional clinical trial monitoring process, which relies heavily on site visits and manual review of accumulative patient data reported through Electronic Data Capture system, is time-consuming and resource-intensive. The recently emerged risk-based monitoring (RBM) and quality tolerance limit (QTL) framework offers a more efficient alternative solution to traditional SDV (source data verification) based quality assurance. These frameworks aim at proactively identifying systematic issues that impact patient safety and data integrity. In this paper, we proposed a machine learning enabled approach to facilitate real-time, automated monitoring of clinical trial QTL risk assessment. Unlike the traditional quality assurance process, where QTLs are evaluated based on single-source data and arbitrary defined fixed threshold, we utilize the QTL-ML framework to integrate information from multiple clinical domains to predict the QTL of variety types at clinical program, study, site and patient level. Moreover, our approach is assumption-free, relying not on historical expectations but on dynamically accumulating trial data to predict quality tolerance limit risks in an automated manner. Embedded within ICH-E6 recommended RBM principles, this innovative machine learning solution for QTL monitoring has the potential to transform sponsors' ability to protect patient safety, reduce trial duration, and lower trial costs.