Digital Twin Implementation for Manufacturing of Adjuvants

Abstract

Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances in the process and comparing the effect on the product critical quality attributes with and without the control of the digital twin. Finally, we showcase the digital twin implementation for the process in good manufacturing practice, through an engineering run, which demonstrated the robustness of the process when controlled by the digital twin.