Accelerating reaction optimization through data-rich experimentation and machine-assisted process development

Abstract

The field of reaction engineering is in a constant state of evolution, adapting to new technologies and the changing demands of process development on accelerated timelines. Recent advancements in laboratory automation, data-rich experimentation, and machine learning have revolutionized chemical synthesis research, bringing significant enhancements to reaction engineering. To showcase these advantages, this study introduces a machine-assisted process development workflow that uses data-rich experimentation to optimize reaction conditions for drug substance manufacturing. The workflow adopts a scientist-in-the-loop approach, ensuring valuable contributions and informed decision-making throughout the entire procedure. Two case studies are presented: a copper-catalyzed methoxylation of an aryl bromide and the global bromination of primary alcohols in gamma-cyclodextrin. In addition to identifying the optimal reaction conditions, the workflow emphasizes the importance of process knowledge. Data-driven reaction models are constructed for both case studies, showcasing how early-stage reaction data can inform late-stage process characterization and control strategies. The speed and efficiency offered by the machine-assisted approach enabled complete reaction optimization and reaction modeling in one week, approximately. This reaction data, along with other process knowledge obtained throughout development, highlight the future prospects for reaction engineering in drug substance development. As the field continues to embrace innovative technologies and methodologies, there is vast potential for further advancements in reaction engineering practices, leading to more streamlined and efficient process development and accelerating the discovery and optimization of chemical manufacturing processes.