Latent Variables-Based Process Modeling of a Continuous Hydrogenation Reaction in API Synthesis of Small Molecules

Abstract

Continuous manufacturing can be benefited by the use of the Quality by Design (QbD) strategy for robust process development and by the use of Process Analytical Technology (PAT) for real-time process monitoring and control. A successful implementation of QbD and PAT for continuous processes relies on a robust and information-rich process model as a basis for process understanding, monitoring, and control. Compared to first principles and other empirical models, a latent variables-based process model is capable of decomposing multidimensional process data into a few orthogonal latent variables and of providing accessible process understanding/visualization and control within the latent variable space. This study is an extension of our group’s earlier effort (Liu et al., J Pharm Innov 6:170–180, 2011) to explore the utility of latent variables-based process modeling in pharmaceutical manufacturing processes. The case presented here is the first application of latent variables-based modeling to a reaction process in small-molecule active pharmaceutical ingredient route synthesis, i.e., a continuous-flow hydrogenation. A particular reactor configuration and operation was used in this proof-of-concept study. It was found that time-variant profiles of pressure in the flow tube reactor served as an effective indicator of gas–liquid interaction within the reactor, thus determining process outcomes, i.e., the extent of reaction and enantiomeric excess (ee), given the importance of process set points. In addition, a design space of process parameters predicted to produce optimal outcomes, i.e., extent of reaction greater than 98 % and ee higher than 93 %, was established in order to provide a flexible operation space for performing the reaction with desired process outcomes. The capabilities of latent variables-based process modeling have been well demonstrated as applied to a continuous-flow hydrogenation reaction, regarding its improved process understanding and the potential for process optimization & control as well. Future efforts will be focused on continuing understanding of the capabilities and limitations of such a methodology on a fully-automated control scheme for continuous flow reaction.