Quality
by design-based crystallization of active pharmaceutical
ingredient (API) particles with optimal properties for lean drug-product
manufacturing remains a long-standing goal of the biopharmaceutical
industry; however, its practical application is difficult. Particle
and powder properties, such as size, shape, and flowability, along
with the key performance indicators of crystallization, such as purity,
yield, and robustness, must be considered for the process design.
However, considering these parameters for a dynamical system, such
as crystallization, is particularly challenging as the involved process
conditions and product attributes are strongly interdependent. This
challenge is addressed herein by applying morphological population
balance (MPB) modeling to the crystallization of an API with a needle-like
morphology and poor powder properties, which pose challenges during
downstream processing. The potential of seeded batch cooling crystallization
is explored for simultaneously improving the particle size and aspect
ratio of the API by manipulating seeding conditions, such as seed
loading and morphology. Moreover, a novel calibration strategy was
developed for the obtained MPB model by performing sequential thermal
cycling–wet-milling experiments. The desired kinetic parameters
were obtained via these experiments with minimal material consumption
and time. The calibrated model was then used to construct probabilistic-design
spaces for different seeding conditions and assess the feasibility
and reproducibility of the crystallization yields. Results revealed
that for persistent needles, relatively small uncertainty in the estimated
kinetic parameters cascades to significant shrinkage of the design
space. Owing to this feature, batch seeded cooling crystallization
is not a suitable platform to control the particle size and morphology
of the API of interest; even if the seeding strategy includes high,
by industrial standards, seed loadings (5–10%) and low aspect
ratio seeds (<2.5). Thus, alternative approaches, such as crystal
nucleation control and/or particle engineering, are required to overcome
the limitations of APIs having persistent needle-like morphologies.
Overall, this study demonstrates a novel paradigm for the calibration
and interrogation of the MPB model, which are essential for their
industrial application and regulatory compliance.