(342e) Reaction Kinetic Modeling and Process Optimization of Multi-Enzyme Biocatalytic Cascade Syntheses | AIChE

(342e) Reaction Kinetic Modeling and Process Optimization of Multi-Enzyme Biocatalytic Cascade Syntheses

Authors 

Stone, K. - Presenter, Merck & Co., Inc.
McMullen, J. P., Merck & Co. Inc.
Grosser, S. T., Merck & Co. Inc.
Forstater, J. H., Merck & Co. Inc.
Hughes, G., Merck & Co. Inc.
Fryszkowska, A., Merck & Co. Inc.
Huffman, M., Merck & Co. Inc.
The application of biocatalysis for the synthesis of new molecules has been significantly enabled in the last decade by advancements in protein engineering. The presence of biocatalysis in pharmaceutical chemistry is rapidly growing due to its uniquely attractive features for highly selective, mild, safe, and sustainable synthesis. Traditional techniques for pharmaceutical reaction development are met with mixed success as they are applied to these systems. As enzyme evolution proceeds alongside traditional process development, potency and selectivity remain elusive properties which threaten reproducibility and continuity across historical data sets. The need for richer information in shorter timescales is further demanded by challenges with material stability, compatibility, and scale.

In this presentation, the challenges and successes of biocatalytic reaction engineering will be summarized with several case studies. Complex enzymatic mechanisms are examined, including for multi-substrate multi-enzyme cascades. Reaction kinetic models are built using DynoChem and are validated against experimental data. The regression of kinetic parameters in these models allows for the demonstration of a statistically robust process understanding; and in some cases, these models allow for useful process prediction and optimization. Often, the model-building process has proven to be useful for identifying gaps in process understanding, which is useful to inform further development and risk mitigation.

This discussion will also include a particular emphasis on the value of pairing PAT and benchtop automation capability with experimental design methodologies to maximize information content in these fast-paced and complex development projects.