Interpretable biomanufacturing process risk and sensitivity analyses for quality‐by‐design and stability control
针对生物制造过程数据有限、变异大的挑战，提出一种基于贝叶斯网络和Shapley值的可解释风险与敏感性分析方法，帮助识别瓶颈、指导工艺设定和数据收集，提升生产稳定性。
Abstract While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality‐by‐design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and product quality attributes, we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most informative data collection, and improve production stability.