Sanofi Pasteur, Val de Reuil, France.
Sanofi Pasteur, Swiftwater, PA, USA.
J Pharm Sci. 2021 Apr;110(4):1540-1544. doi: 10.1016/j.xphs.2021.01.016. Epub 2021 Jan 23.
A wide variety of computational models covering statistical, mechanistic, and machine learning (locked and adaptive) methods are explored for use in biopharmaceutical manufacturing. Limited discussion exists on how to establish the credibility of a computational model for application in biopharmaceutical manufacturing. In this work, we tried to use the American Society of Mechanical Engineers (ASME) Verification and Validation 40 (V&V 40) standard and FDA proposed AI/ML model life cycle management framework for Software as a Medical Device (SaMD) in biopharmaceutical manufacturing use cases, by applying to a set of curated hypothetical examples. We discussed the need for standardized frameworks to facilitate consistent decision making to enable efficient adoption of computational models in biopharmaceutical manufacturing and alignment of existing good practices with existing frameworks. In the study of our examples, we anticipate existing frameworks like V&V 40 can be adopted.
各种涵盖统计、机械和机器学习（锁定和自适应）方法的计算模型都被探索用于生物制药制造。对于如何为生物制药制造应用建立计算模型的可信度，目前讨论有限。在这项工作中，我们试图在生物制药制造用例中使用美国机械工程师协会（ASME）验证和确认 40（V&V 40）标准和 FDA 提出的人工智能/机器学习模型生命周期管理框架，适用于一组经过策划的假设示例。我们讨论了需要标准化框架来促进一致的决策，以便在生物制药制造中有效地采用计算模型，并使现有良好实践与现有框架保持一致。在我们的示例研究中，我们预计可以采用现有的框架，如 V&V 40。
