2025 AIChE Annual Meeting

(158f) System Model for Continuous Manufacturing of an API in an Industrial Environment

Authors

Reza Amirmoshiri, Rice University
Antonio Benedetti, GlaxoSmithKline
Giulia Marchese, GlaxoSmithKline (GSK)
Marco Quaglio, PolyModels Hub
Yasser Jangjou, University of Houston
Mathematical mechanistic and hybrid modeling is becoming an important part of modern pharmaceutical manufacturing, providing a systematic approach to understanding, optimizing, and controlling complex processes. Its integration within the industry is closely aligned with the principles of Quality by Design (QbD), enabling a comprehensive understanding of manufacturing dynamics to ensure consistent product quality (Christodoulou et al., 2023). Accurate model-based predictions of product quality require quantifying the influence of various inputs across multiple unit operations. This necessitates an integrated model; a System Modelling framework which interconnects individual unit operations to capture their interactions and overall impact on the process (Diab et al., 2022).

In this work, we present an integrated end-to-end System Model for the continuous manufacturing of a high-value active pharmaceutical ingredient (API) at commercial scale. This model facilitates optimization of process conditions to maximize yield while ensuring product quality. The dynamic modeling framework was developed in Python using the PolyModels Hub package and includes critical unit operations for reaction and liquid-liquid separation steps. We will discuss the numerical methods for solving the complex system of Partial Differential Equations (PDEs) and Population Balance Models (droplet dispersions) appearing in the End-to-End System Model.

The primary aim of the work is to accurately predict API yield, as well as the formation and removal of impurities identified as Critical Quality Attributes (CQAs). Parameter estimation (model calibration), validation and sensitivity analysis capabilities were developed for the continuous flow process in Python. We have also defined and evaluated multiple optimization scenarios within approved parameter ranges to recommend optimal process conditions for enhanced API production.

In this work, we illustrate the versatility of the System Modeling framework across three critical activities in pharmaceutical development and manufacturing: 1) design space characterization, 2) process monitoring, and 3) active process control following the review paper by Destro and Barolo (2022). Finally, we outline future research opportunities to further integrate and leverage mathematical modeling within industrial continuous pharmaceutical processes.

References

Christodoulou, C., Diab, S., Bano, G., Aroniada, M., Hodnett, N. and Zomer, S., 2023. The Integration of Drug Substance and Drug Product Manufacturing Models: The Missing Link for Model-based End-to-End Process Development. In Computer Aided Chemical Engineering (Vol. 52, pp. 2101-2106). Elsevier.

Destro, F. and Barolo, M., 2022. A review on the modernization of pharmaceutical development and manufacturing–Trends, perspectives, and the role of mathematical modeling. International journal of pharmaceutics, 620, p.121715.

Diab, S., Bano, G., Christodoulou, C., Hodnett, N., Benedetti, A., Andersson, M. and Zomer, S., 2022. Application of a system model for continuous manufacturing of an active pharmaceutical ingredient in an industrial environment. Journal of Pharmaceutical Innovation, 17(4), pp.1333-1346.