Automated End-to-End Workflow for Volumetric Mass-Transfer Coefficient (k_La) Characterization in Small-Molecule Pharmaceutical Development

Biocatalytic aerobic oxidations have recently emerged in the small-molecule pharmaceutical industry as a selective and green alternative to traditional chemocatalyzed oxidations that rely on transition-metal-based oxidants. Engineering aspects, such as mass transfer and reactor design, play a key role in determining the adoption and success of such biocatalytic aerobic oxidations; therefore, a detailed understanding and characterization of mass transfer at various process scales become a critical component of reaction development. Traditional mass-transfer characterization techniques are tedious to execute for a large number of processing conditions and reactor configurations and require careful experimental planning and data capture to ensure accurate regression of the volumetric mass-transfer coefficient, k(L)a. A custom-automated reactor characterization workflow has been developed to surmount these challenges and enable rapid design space characterization of mass transfer in reactors spanning the laboratory to production scales. Custom data compression and processing scripts paired with automated parameter regression enable rapid data processing and regression of experimental k(L)a values. Furthermore, a detailed analysis of system lag and error was conducted to enable more accurate modeling of system start-up during k(L)a experiments. This workflow has been leveraged to build a database of more than 2000 unique processing conditions in small-molecule centric, traditional batch chemistry reactors spanning laboratory development through manufacturing. This database allows scientists to quickly determine suitable processing conditions with a known k(L)a and mass-transfer performance, lowering the barriers to adoption and enabling rapid biocatalytic aerobic oxidation reaction development and scale-up.