Synthetic molecules need to be developed faster and at a lower cost than biopharmaceutical drugs. This increases the pressure on chemists and engineers to develop faster and more sustainable processes, which requires that they follow Green Chemistry principles. One key societal pressure is to expedite new drug development. Traditionally, API chemical process development takes, on average, six to eight years of the total five to 15 that is takes to bring a drug from early discovery to the commercial stage. However, the industry is being pushed to cut this in half.

Chemical processes vary in complexity and involve many unit operations and variables that need to be considered. Once a process is developed at laboratory scale, it must be scaled up and transferred to production, introducing further complexity and variables. To accelerate the pace, there is a growing emphasis on leveraging technologies to enhance experimentation and data management. This includes using automated systems for experimentation, electronic notebooks to enable digitalisation, and Artificial Intelligence (AI) and machine learning (ML) algorithms to expedite process development. Furthermore, digital twins are emerging as a valuable tool to mitigate risk and expedite scale-up processes.

The rush towards rapid deployment must be balanced with the development and optimisation of robust and sustainable processes. This requirement is a challenge for CDMOs, yet also presents an opportunity to capitalise on the historical knowledge gathered from the different drugs that they manufacture over the years. Using this historical data and leveraging AI, ML and other tools, they can identify and implement previous learnings, thereby accelerating process development.

Read the full article at specchemonline.com