(364l) Integrated Modeling and Experimental Design for the Digital Design of Pharmaceutical Manufacturing Processes

Mathematical modeling and optimization, Process optimization under uncertainty, Machine learning, Process synthesis and optimization, Pharmaceutical manufacturing

Research abstract:

Crystallization is an integral purification and particle control operation in the pharmaceutical industry that influences critical product attributes such as crystal size distribution, yield, and polymorphic form. The pharmaceutical industry demands well-designed and controlled crystallization processes to consistently produce crystals with desired attributes. While research has explored the potential of model-based design and control strategies to tackle this challenge, the development of frameworks enabling the systematic construction of a model-based digital twin remains an ongoing pursuit.

Toward this goal, in this research, a deterministic population-balance-based framework was used to model crystallization processes, and a kinetic parameter estimation framework was developed to estimate the crystallization kinetics and the associated parameter uncertainties from experimental data.¹

After studying the impact of kinetic parameter uncertainties on the design of crystallization processes, the framework was extended to incorporate the impact of kinetic parameter uncertainty in optimal process design through a chance-constrained programming approach. A nested two-level simulation-optimization approach using surrogate modeling was used to solve the resulting chance-constrained optimization problem. ² Furthermore, to ensure the development of accurate mechanistic models, iterative model-based experimental design (IMED) frameworks were also developed, facilitating automated mechanistic model identification and optimal experimental design to enhance parameter precision.³

Lastly, with the ongoing batch-to-continuous transition in the pharmaceutical industry, a hybrid rule-based and deterministic optimization-driven process decision framework was developed for the analysis and optimization of pharmaceutical flowsheets for end-to-end optimal (E2EO) pharmaceutical manufacturing.⁴ Overall, this research aims to explore mechanistic, data-driven, and hybrid modeling methodologies for designing targeted and adaptable digital twins for pharmaceutical processes.

References:

1. Barhate Y., Kilari H., Wu, W.-L. & Nagy, Z. K. Population balance model enabled digital design and uncertainty analysis framework for continuous crystallization of pharmaceuticals using an automated platform with full recycle and minimal material use. Eng. Sci. 287, 119688 (2024).
2. Barhate Y., Nagy Z.K., Reliability-based optimal control of crystallization systems under uncertainty. IFAC –PapersOnLine, 2024.
3. Barhate Y., Kilari H., Nagy Z.K., Automated model-based experimental design procedure for robust digital twin development for continuous crystallization systems. AIChE Annual Meeting, 2023.
4. Barhate Y., Laky D., Casas-Orozco D., Reklaitis G., Nagy Z.K., Rule-based decision framework for the digital synthesis of optimal pharmaceutical processes. Computer Aided Chemical Engineering, 53, 1315-1320, (2024).