Recent developments of automated flow chemistry in pharmaceutical compounds synthesis

Abstract

Recent developments in automated flow chemistry for pharmaceutical compound synthesis have garnered significant attention. Automation in synthesis represents a cutting-edge frontier in the field of chemistry, offering highly efficient, rapid, and reproducible synthetic methods that significantly shorten reaction time and reduce costs. In the realm of pharmaceutical compound synthesis, automated flow chemistry demonstrates unique importance. By utilizing flow chemistry, reactions can be performed under continuous flow conditions, enabling precise reaction control, higher yields, and increased product purity. Additionally, automated flow synthesis overcomes several challenges encountered in traditional batch synthesis, such as decreased generation of chemical waste, optimization of reaction conditions, and enhanced operational safety. This review highlights the recent developments in automated flow synthesis of various pharmaceutical compounds, including large biopharmaceutical molecules, small organic drug molecules, and carbohydrates. It covers automated iterative synthesis and the use of machine learning to enhance synthesis efficiency. Furthermore, it explores the practical application of high-throughput synthesis and screening technologies. Finally, the review offers concise perspectives on potential future developments in the field.

Graphical abstract

The development of automated flow synthesis kept breaking through new challenges for chemical reactions. Especially with the increasing demand for fast and efficient synthesis of therapeutic compounds, automated systems built a solid foundation for pharmaceutical innovation.

Solid-phase flow synthesis has been well-developed in the synthesis of large biopharmaceutical molecules; the immobilized support helps replace tedious separation and purification with a simple solvent wash. Additionally, flow-based pathways could provide convenience for automation.

High-throughput synthesis with in-line analysis offers both high-efficiency production and accurate monitoring. Therefore, this combination could be easily applied to rapid screening processes for building a large library, enhancing the performance of machine learning in reaction, and product prediction.

Artificial intelligence can be applied to self-optimized synthesis processes. Algorithm-based software could rapidly calculate and optimize insufficient reactions with a learning model built on past reactions posted in the literature. The connected robotic arm can then be automatically set to perform the optimized reaction.