The API process development time conundrum. Is scalable technology the solution?

The pressure to bring a new drug to market has increased significantly but the develop time needed has also increased because molecules are more complex to make. Chemists need to compress time by working in parallel on difficult steps. Process optimization is not always completed in time and a less desirable manufacturing processes is used to deliver clinical trials material. Further optimization can happen later, but once filed, a registered process is difficult to change. Therefore, using scalable technologies early in development to reduce the need to redesign the process later is critical. However, technologies are not always available, well understood, or too expensive to be considered.
Chromatography is one of these. This unit operation is used at all development stages but is often discarded as a solution for commercial production.

Chromatography is mostly known and used for being a very powerful analytical tool. All development chemists check reaction completion and impurity profiles using an HPLC method. Every API is tested for purity using a chromatography method (HPLC or UPLC). Residual solvents are also identified and quantified by chromatography (GC). It is a very powerful tool that can separate the most difficult mixtures (enantiomers, peptides, polymers…). It is equally a formidable production tool used in many industries. For example, continuous ion exchange SMB processes are used to purify the sugar we consume (1). If the food industry is using SMB for manufacturing a product with tremendous price pressure, it is because the technology is robust, reliable, scalable, and very efficient, resulting in a very low operating cost at commercial scale. However, when it comes to API manufacturing, process development chemists are reluctant to use chromatography as a long-term solution and will spend a significant amount of development time to remove chromatography from the synthetic route used in early development. This is likely due to a lack of understanding the optimization process required to bring this technology to commercial scale efficiently and cost effectively.