细胞治疗作为一种新兴的治疗策略，需要科学的监管框架，但由于风险分类缺乏全球共识，因此面临基于风险的监管挑战。本研究应用贝叶斯网络分析来比较和评估美国食品药品监督管理局 (FDA)、厚生劳动省 (MHLW) 和世界卫生组织 (WHO) 提出的细胞产品风险分类策略，使用真实的世界数据来验证模型。在三个监管框架内评估关键风险因素的适当性及其对临床安全的影响。结果表明了改进风险分类方法的几个方向。此外，一项子研究重点关注特定类型的细胞和基因疗法 (CGT)，即嵌合抗原受体 (CAR) T 细胞疗法。它强调了在评估 CAR T 细胞产品的安全风险时考虑 CAR 靶点、肿瘤类型和共刺激领域的重要性。总体而言，目前蜂窝产品缺乏基于真实数据的监管框架，也缺乏基于风险的分类审查方法。本研究旨在完善细胞产品的监管体系，强调基于风险的分类。此外，该研究主张在监管科学中利用机器学习来加强对细胞产品安全性的评估，说明贝叶斯网络在协助细胞产品风险分类的监管决策中的作用。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products.Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.