(555a) Optimization-Based Digital Design for Agglomeration Control of a Pharmaceutical Crystallization Process

The key aspect of a successful crystallization process lies in achieving precise control of desired crystalline product quality such as particle size and distribution, morphology, and degree of agglomeration. Agglomeration is often considered undesirable as the agglomerates increase the risk of impurity incorporation into the crystalline product due to solvent inclusion [1, 2, 3]. Additionally, agglomeration leads to longer filtration and drying times with low drug product manufacturability (flowability, bulk density, etc.) and poses challenges in particle size or content uniformity. Controlling the degree of supersaturation and agitation rate demonstrated improved agglomeration control in general [1,4]. Alternatively, a more specialized approach involves implementing thermocycles that facilitates deagglomeration along with dissolution of fines that could arise as a result of other secondary mechanisms such as breakage or attrition.

Determining an optimal heating-cooling recipe with number of heating-cooling cycles, minimum and maximum temperatures as design variables forms a complex optimization problem. Model free- quality-by-control (QbC) approach using process analytical technology (PAT) would be more resource intensive in such complex factorial design problems [5,6]. Employing population balance model (PBM) for monitoring and control of agglomeration proved efficient in several reported crystallization studies [7, 8]. Hence, this study explores the model-based approach for determination of optimal temperature recipe towards minimizing the degree of agglomeration and increasing the crystal size.

In this work, the number density of agglomerates is coupled with the PBM of the total number density to monitor the evolution of the degree of agglomeration during the thermocycles. Several mechanisms are considered in the hybrid PBM approach, including nucleation, growth, dissolution, agglomeration, and deagglomeration. The applicability of this hybrid PBM approach is demonstrated for crystallization of industrial active pharmaceutical ingredient (API), known as Compound K. The majority of parameters are estimated with the initial design of experiments (DoE) based on the iterative model-based experimental design approach in a previous study. The dissolution parameters are estimated based on additional thermocycle experiments added for parameter estimation. Based on the results from the in-silico design of experiments (DoE), the hybrid approach provides a superior method for comparing the process performance based on temperature recipes when agglomeration is involved. This is in contrast to methods that evaluate the degree of agglomeration solely based on the total number of bridges between particles.

In conclusion, the quantification of degree of agglomeration in this study provided an effective way for monitoring the agglomeration during crystallization process. The optimization-based digital design using the developed model yielded a thermocycle profile that showed an improvement in the degree of agglomeration compared to linear cooling profiles. Furthermore, it outperformed a benchmark recipe designed previously based on quality-by-design approach that showed a slightly higher degree of agglomeration with a longer batch time.

Reference:

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