Efficient and selective oxidation reactions remain a significant unmet need in synthetic chemistry and drug manufacturing due to limitations of conventional methods related to catalyst toxicity, selectivity, and safety, frequently resulting in poor atom efficiency. Biocatalytic aerobic oxidations offer enormous potential to address these challenges by leveraging enzyme’s high selectivity and mild reaction conditions, enabling more efficient reactions with reduced environmental impact and improved safety profiles. The breadth of transformations catalyzed by oxygen-dependent enzymes can unlock novel synthetic pathways in pharmaceutical manufacturing, leveraging enzyme catalyzed C-H activation, alcohol and amine oxidation, or ATP-recycling.
One of the core challenges to wider adoption of biocatalytic aerobic oxidations is the impact of gas-liquid mass transfer on reaction performance. Engineering and reactor design considerations are critical during reaction development and scale-up. By characterizing the volumetric mass transfer coefficient kLa, a parameter that describes the rate at which a gas is transferred into a liquid phase, we can design and optimize reactor systems to ensure adequate oxygen transfer, optimal reaction rates, and predictable performance across scale. We have invested in rigorous kLa characterization and developed an innovative, fully-automated methodology to extensively characterize mass transfer generating a database of >2000 unique kLa values in reactors spanning laboratory to manufacturing scales. Furthermore, a machine-learning-based model was created to predict kLa and enable autonomous reactor characterization, ensuring efficient design space exploration and reducing the number of necessary kLa trials. The machine-learning augmented database has been deployed as a user-friendly web-app to give scientists easy access to reactor mass transfer data necessary for process development.
To date, this methodology has enabled successful demonstrations of biocatalytic oxidation processes for several pipeline projects including glycosylations, hydroxylations, desymmetrization reactions and ATP-recycling. As the toolbox of oxygen-dependent biocatalysts continues to expand, this methodology lays the groundwork for the development of future enzymatic aerobic oxidations at scale and reduces the environmental footprint of manufacturing scale redox transformations.
Keith Mattern is currently an Associate Principal Scientist working in the Enabling Technologies group at MSD and is a member of the Data Rich Experimentation (DRE) Team. He received his BS/MS degrees in chemical engineering from Bucknell University prior to joining MSD in 2016. He has spent most of his career working on the scale-up of small molecule API’s, most notably with a concentration on the engineering aspects of scaling biocatalytic aerobic oxidations. Keith focuses on both the development, application, and deployment of new tools and technologies and strives to embed data-intense tools into the fabric of his organization’s process development culture. His focus on hardware and software automation and integration led to the development of automated platform methodologies and techniques used to characterize mass transfer in reactors across development and manufacturing scales to support the successful scale-up of a number of important pipeline biocatalytic processes.