(515d) Automated Process in Continuous Pharmaceutical Manufacturing through Real-Time Process Monitoring and Disturbance Detection Using PAT and Online First-Principle Models

With the increased interest in continuous manufacturing (CM) of pharmaceutical processes, implementation of process automation through real-time process monitoring technology plays a critical role in ensuring product quality. The strategy for monitoring and automating continuous synthesis processes through a combined application of process analytical technology (PAT) and online first-principle modelling is discussed for enhanced process robustness. Product concentration predictions were achieved in real-time through PAT implementation using IR and Raman spectroscopy coupled with online chemometric analysis. The process data such as reactor temperatures, starting material concentrations, and stream flow rates were utilized for the computational fluid dynamics (CFD) analysis for calculating the product concentrations based on reaction kinetics as well as mass and heat transfer principles. Process disturbance experiments were conducted for a multi-step flow synthesis process and the changes in the product concentrations were monitored by PAT and the first-principle model. This in-silico modelling approach using both the empirical and theoretical models provides robust process monitoring technology which can be utilized in the process decision making such as material diversion and feedback control actions.