Biomanufacturing plays a crucial role in supporting the economy and ensuring public health. However, it faces several challenges including complexity, high variability, lengthy lead time and limited process data, especially for personalized new cell and gene biotherapeutics. To address these challenges, the authors propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory-based risk and sensitivity analysis for production processes to facilitate quality-by-design and stability control. The authors first explore the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs) by creating a Bayesian network-based probabilistic knowledge graph. This graph characterizes the complex causal interdependencies of all factors involved in the bioprocess. By leveraging this graph, they introduce a Shapley value-based sensitivity analysis that accurately quantifies the variation contribution from each input factor on the outputs such as productivity and product quality. Since the coefficients of the bioprocess model are learned from limited process observations, the authors derive a Bayesian posterior distribution to quantify model uncertainty. They further develop the Shapley value-based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. This approach allows for identifying bottlenecks in the bioprocess, guiding reliable process specifications, determining informative data collection strategies, and improving production stability. The proposed bioprocess risk and sensitivity analyses provide valuable insights into understanding and managing risks associated with biomanufacturing processes. These analyses can help optimize production processes by identifying critical factors that influence productivity and product quality. Additionally, they enable decision-makers to make informed choices regarding process specifications based on reliable data collection strategies. Overall, this research contributes to enhancing the stability and reliability of biomanufacturing processes while ensuring consistent product quality.
Biomanufacturing is important for the economy and public health. It can be challenging because it is complex, has a lot of variation, takes a long time, and has limited data. The authors have created a special graph to help with these challenges. They also use game theory to analyze risks and make sure the process is stable. They use another type of graph to understand how different factors affect the quality of the product. They also analyze uncertainty in their models. These analyses help find problems, set specifications for the process, collect good data, and make production stable. This research makes biomanufacturing more reliable and ensures good product quality."
Definitions
- Biomanufacturing: Making things using living organisms.
- Economy: How money is made and used in a country or area.
- Public health: Taking care of everyone's well-being.
- Complexity: Something that is complicated or hard to understand.
- Variability: How much something changes or varies.
- Lead time: How long it takes for something to happen or be done.
- Process data: Information about how something is made or done.
- Semantic bioprocess probabilistic knowledge graph: A special way of organizing information about making things using living organisms.
- Game theory-based risk and sensitivity analysis: Using math to study risks and how sensitive something is to changes.
- Causal relationships: How one thing causes another thing to happen.
- Interactions: When two or more things affect each other.
- Critical process parameters: Important
Biomanufacturing: Challenges and Solutions
Biomanufacturing plays a crucial role in supporting the economy and ensuring public health. However, it faces several challenges including complexity, high variability, lengthy lead time and limited process data, especially for personalized new cell and gene biotherapeutics. To address these issues, researchers have proposed an interpretable semantic bioprocess probabilistic knowledge graph and developed a game theory-based risk and sensitivity analysis for production processes to facilitate quality-by-design and stability control.
Exploring Causal Relationships with Bayesian Networks
The authors first explore the causal relationships between critical process parameters (CPPs) and quality attributes (CQAs) by creating a Bayesian network-based probabilistic knowledge graph. This graph characterizes the complex causal interdependencies of all factors involved in the bioprocess. By leveraging this graph, they introduce a Shapley value-based sensitivity analysis that accurately quantifies the variation contribution from each input factor on outputs such as productivity and product quality.
Quantifying Model Uncertainty with Bayesian Posterior Distribution
Since the coefficients of the bioprocess model are learned from limited process observations, the authors derive a Bayesian posterior distribution to quantify model uncertainty. They further develop Shapley value-based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. This approach allows for identifying bottlenecks in the bioprocess, guiding reliable process specifications, determining informative data collection strategies, and improving production stability.
Benefits of Bioprocess Risk & Sensitivity Analysis
The proposed bioprocess risk & sensitivity analyses provide valuable insights into understanding & managing risks associated with biomanufacturing processes. These analyses can help optimize production processes by identifying critical factors that influence productivity & product quality while enabling decision makers to make informed choices regarding process specifications based on reliable data collection strategies. Overall, this research contributes to enhancing stability & reliability of biomanufacturing processes while ensuring consistent product quality.
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 quality attributes (CPPs/CQAs), 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.
