Introduction
Crystallization has been an important purification technique in the chemical and pharmaceutical industries for many years with over 90% of active pharmaceutical ingredients (APIs) being purified with one or more crystallization steps (1, 2). Crystallizer operation and control have continued to increase in importance over recent years due to continually increasing stringency of final product requirements for APIs by government agencies worldwide. The crystallization process can affect multiple crystal critical quality attributes (CQAs) including yield and purity (3-5), polymorph control (6, 7), and particle size distribution (PSD) (8) all of which can lead to variations in final product performance by affecting solubility, dissolution rate, and tablet hardness of final APIs (9). However, the increased levels of control required to continue to meet these standards cannot be realized without a fundamental understanding of key operating parameters of crystallization equipment including mixing uniformity and scale up (10).
As it is well known, batch crystallization is the oldest and most commonly used technique. However, adoption of continuous approaches are slowed by 1) existing know-how on batch processes, 2) preference towards relying on already expensed equipment instead of making new investments, 3) revalidation hurdles of converting existing API production processes to flow (2), and 4) lack of technological solutions that provide enough compelling arguments to encourage new technolgy adoption.
Despite the dominance of batch based processes, the USFDA has been showing increased support for continuous manufacturing equipment (11-13) as batch processes are known to have numerous shortcomings, such as issues with scale up, batch-to-batch inconsistencies, non-uniform mixing conditions, and high manufacturing and maintenance costs (2, 14-19). In comparison, continuous crystallization techniques – most commonly utilized through mixed solids mixed product removal (MSMPR) crystallizers – are known to be easier to scale up (20) and can operate at a higher level of supersaturation due to an increased uniformity of mixing (21).
Additionally, increasing the number of tanks in a cascade of MSMPRs is known to provide benefits to the CQAs due to the narrowing of the residence time distribution as the crystals fundamentally have varying probabilities of residence time in the vessel (22-25) as shown in Figure 1.
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