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

TL;DR

This paper introduces a probabilistic framework using polynomial chaos theory to analyze and optimize continuous lyophilization processes, effectively managing uncertainties in critical process variables for improved control and design.

Contribution

It develops a PCT-based model for uncertainty quantification in continuous lyophilization and demonstrates its integration into stochastic optimization and control strategies.

Findings

Accurately quantifies uncertainty effects on temperature, sublimation front, and bound water concentration.

Enhances process design and control by incorporating probabilistic uncertainty.

Provides a computationally efficient approach for uncertainty analysis in lyophilization.

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.