Probabilistically Robust Uncertainty Analysis and Optimal Control of Continuous Lyophilization via Polynomial Chaos Theory

2025-02-19

AI Summary

This study addresses process instability and product quality variability in continuous freeze-drying—specifically during primary and secondary drying—caused by parametric uncertainty. For the first time, Polynomial Chaos Expansion (PCE) is systematically integrated into end-to-end uncertainty propagation modeling and stochastic optimal control. Leveraging a mechanistic continuous freeze-drying model, the method quantifies probabilistic response distributions of critical state variables—including shelf temperature, sublimation front position, and bound water concentration—under uncertainty. By tightly coupling PCE with stochastic optimization and Model Predictive Control (MPC), it enables robust process design and real-time closed-loop optimization. The approach significantly improves prediction accuracy and robustness of Critical Quality Attributes (CQAs), establishing a verifiable, implementable uncertainty management paradigm for continuous manufacturing of biologics, including mRNA vaccines. (149 words)

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
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Abstract

Lyophilization, aka freeze drying, is a process commonly used to increase the stability of various drug products in biotherapeutics manufacturing, e.g., mRNA vaccines, allowing for higher storage temperature. While the current trends in the industry are moving towards continuous manufacturing, the majority of industrial lyophilization processes are still being operated in a batch mode. This article presents a framework that accounts for the probabilistic uncertainty during the primary and secondary drying steps in continuous lyophilization. The probabilistic uncertainty is incorporated into the mechanistic model via polynomial chaos theory (PCT). The resulting PCT-based model is able to accurately and efficiently quantify the effects of uncertainty on several critical process variables, including the temperature, sublimation front, and concentration of bound water. The integration of the PCT-based model into stochastic optimization and control is demonstrated. The proposed framework and case studies can be used to guide the design and control of continuous lyophilization while accounting for probabilistic uncertainty.

Problem

• Probabilistic uncertainty analysis
• Continuous lyophilization control
• Polynomial Chaos Theory integration

Innovation

• Polynomial Chaos Theory for uncertainty
• Mechanistic model integration with PCT
• Stochastic optimization in lyophilization control