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The design of pharmaceutical crystallization processes is a challenging engineering problem because of the specific and versatile quality requirements of the end-product, amplified by the tight regulatory standards. The current industrial standard for crystallization process design is based on the use of the quality-bydesign (QbD) framework, which relies on factorial design of experiments (DoE). Hence, QbD inherently generates a large number of resource-consuming open loop crystallization experiments. This is especially true when more complex operating conditions need to be designed, such as temperature cycles, which require a large number of decision variables in the DoE. In contrast, the recently proposed quality-by-control (QbC) approach relies on feedback control algorithms to directly achieve the desired product properties by manipulating the appropriate process conditions. The first aim of this work is to demonstrate the effectiveness of a model-free feedback control strategy, referred to as model-free (mf) QbC. Direct nucleation control (DNC) and supersaturation control (SSC) are applied as a part of the mfQbC approach, which, ideally, requires only two feedback control experiments to obtain a temperature profile that results in obtaining the desired product quality. Although mfQbC provides a rapid process design, it is often suboptimal. In addition, it is shown that the experimental data generated by mfQbC can be used for process model development and kinetic parameter estimation. The validated model enables optimization-based design using the model-based (mb) QbC framework. For this case study, a population balance (PB) based process model is developed, which involves primary and secondary nucleation, growth, and dissolution, as well as a novel formulation of agglomeration, and deagglomeration of crystals. In addition to taking into account the agglomeration, the number of agglomerates is also tracked as a balance between the agglomeration and deagglomeration events. The kinetic parameters are estimated using a novel objective function formulation relying on the minimization of the difference between the measured and simulated concentrations and crystal size distributions (CSDs) and the maximization of the correlation between the simulated crystal number density and measured crystal count data obtained from focused beam reflectance measurement (FBRM). The kinetic parameters are identified based on the experimental data generated from the mfQbC, which inherently reduced the experimental effort required for the model development. The temperature profile is optimized for the fine index and agglomeration degree minimization. The repeated open-loop implementation of mfQbC-and mbQbC-designed processes showed that the batch-to-batch variation is low and the product quality is high in both cases. The proposed general framework is illustrated for the systematic quick and optimal design of crystallization processes that require temperature cycles with a low number of experiments.

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Control of crystal size distribution (CSD) and shape is critical in the pharmaceutical industries for meeting tight critical quality attribute (CQA) requirements in the manufacturing of active pharmaceutical ingredients (APIs). In general, to increase the efficiency of downstream operations such as filtration and drying, and increase the flowability and manufacturability of powders, large crystals with a low aspect ratio (AR) are preferred. Large AR, needle-shaped crystals are very common in the pharmaceutical industries; consequently, to achieve desired manufacturability performance, a careful design of the crystallization processes is required. In this work, the systematic design of a crystallization process for an API (compound A) from Takeda Pharmaceuticals International Co. is demonstrated. The challenges related to the crystallization of compound A include that the process is nucleation dominated by slow growth rates, which necessitates intermittent internal fines removal via temperature cycling. Moreover, compound A tends to form high AR crystals, which can cause manufacturability problems. The aim of the crystallization design is to produce low AR (<3) and sufficiently large crystals with narrow distributions. Two methods were applied to reach these goals: (1) application of immersion milling to further control the shape and size of crystals and (2) application of temperature cycles to internally remove the fines. It is also demonstrated that these approaches can be implemented in an unseeded crystallization without compromising the product quality.

Hot melt extrusion (HME) to prepare amorphous solid dispersions (ASDs) at temperatures below the drug's melting point requires the crystalline drug to dissolve into the molten polymer. This requires an understanding of the drug's solubility in the molten polymer as well as amorphization (crystal dissolution) kinetics. The goal of this study was to identify drug crystal attributes which contribute to rapid amorphization during hot melt extrusion processing to form ASDs. Particle engineering approaches were used to recrystallize bicalutamide with different particle size distributions and defect density. These lots were then used to prepare ASDs by HME to monitor the amorphization kinetics. Particle size had the expected effect on the amorphization rate, and defect density was also observed to accelerate amorphization. A population balance model using dissolution and breakage phenomena was developed to investigate the dynamic evolution of crystal size distribution during a hot melt extrusion process, and parameter estimation was utilized to simulate the experimental HME results. Breakage kinetics were found to dominate the crystal dissolution process, synergistically accelerated by particles with high defect density. The findings have implications for particle engineering of crystals to enable the hot melt extrusion process, as well as improved process modeling through incorporating particle attributes.

Prediction and control of the product properties in crystallization processes are practical challenges in the pharmaceutical industry. Effective crystallization process design and operation techniques are needed to meet the critical quality attributes (CQAs) and minimize batch-to-batch variation. Mathematical modeling can enhance process understanding and save a considerable amount of time, effort and raw material when used in process development following the guidelines of the Quality-by-Design (QbD) framework. When the mathematical model of the process is fitted and validated with experimental data, it provides a digital twin of the process that enables execution of in silico design of experiments (DoEs), which is particularly beneficial when the number of factors increases or if the material is expensive or sparingly available, e.g., during early stage development. This work presents the benefits of crystallization process modeling by studying an active pharmaceutical ingredient (API) from Takeda Pharmaceuticals International Co., referred hereafter as Compound A. A framework for crystallization model construction, parameter estimation and validation are demonstrated through the case study of Compound A by using the population balance modeling (PBM) approach. Secondary nucleation, size dependent growth (SDG), and size dependent dissolution mechanisms are considered. Size dependency is introduced with a new formulation capturing the considerably slower growth of small crystals (D 90 < 10 μm) while having size dependency for the larger crystal size domain (D 90 > 200 μm) similar to the models from the literature. To make the model parameter estimation more industrially relevant, a novel method developed recently by the authors is applied to use the focused beam reflectance measurement (FBRM) data directly in the parameter estimation without further transformations. The kinetic parameters are estimated by minimizing the difference between measured and simulated concentrations, crystal size distributions (CSDs) and maximizing the correlation between the simulated crystal number density and measured FBRM counts. The paper also illustrates that the novel SDG rate expression can capture the CSD dynamics considerably better than the standard SDG rate models. The digital twin is used for in silico DoE and process optimization, and the simulation results are validated experimentally, demonstrating the benefits of model-based digital design for crystallization process development.

Precompetitive collaborations on new enabling technologies for research and development are becoming popular among pharmaceutical companies. The Enabling Technologies Consortium (ETC), a precompetitive collaboration of leading innovative pharmaceutical companies, identifies and executes projects, often with third-party collaborators, to develop new tools and technologies of mutual interest. Here, we report the results of one of the first ETC projects: the development of a user-friendly population balance model (PBM)-based crystallization simulator software. This project required the development of PBM software with integrated experimental data handling, kinetic parameter regression, interactive process simulation, visualization, and optimization capabilities incorporated in a computationally efficient and robust software platform. Inputs from a team of experienced scientists at 10 ETC member companies helped define a set of software features that guided a team of crystallization modelers to develop software incorporating these features. Communication, continuous testing, and feedback between the ETC and the academic team facilitated the software development. The product of this project, a software tool called CrySiV, an acronym for Crystallization Simulation and Visualization, is reported herein. Currently, CrySiV can be used for cooling, antisolvent, and combined cooling and antisolvent crystallization processes, with primary and secondary nucleation, growth, dissolution, agglomeration, and breakage of crystals. This paper describes the features and the numerical methods of the software and presents two case studies demonstrating its use for parameter estimation. In the first case study, a simulated data set is used to demonstrate the capabilities of the software to find kinetic parameters and its goodness of fit to a known solution. In the second case study, the kinetics of an antisolvent crystallization of indomethacin from a ternary solvent system are estimated, providing a practical example of the tool.

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