Abstract
Active pharmaceutical ingredient (API) particle size distribution is important for both downstream processing operations and in vivo performance. Crystallization process parameters and reactor configuration are important in controlling API particle size distribution (PSD). Given the large number of parameters and the scale-dependence of many parameters, it can be difficult to design a scalable crystallization process that delivers a target PSD. Population balance modeling is a useful tool for understanding crystallization kinetics, which are primarily scale-independent, predicting PSD, and studying the impact of process parameters on PSD. Although population balance modeling (PBM) does have certain limitations, such as scale dependency of secondary nucleation, and is currently limited in commercial software packages to one particle dimension, which has difficulty in predicting PSD for high aspect ratio morphologies, there is still much to be gained from applying PBM in API crystallization processes.
Highlights
The preferred method for purification, form, and particle size distribution (PSD) control of an active pharmaceutical ingredient (API) is crystallization [1]
The PSD is discretized into particle size “bins” (50 for the BMS compound A case study), and, using the seed PSD as the initial condition, and the nucleation rates as the boundary condition, the equation is solved for each size bin utilizing a one-dimensional finite volume method via gPROMS FormulatedProducts, or other software platforms
We have found that a reasonably accurate generated with a minimum of four data sets of seed size quantiles, API solution concentration during model can be generated with a minimum of four data sets of seed size quantiles, API solution crystallization, and final product quantiles
Summary
The preferred method for purification, form, and particle size distribution (PSD) control of an active pharmaceutical ingredient (API) is crystallization [1]. Due to the scale-dependency of this approach, the common practice is to first establish a suitable set of process parameter ranges that can robustly deliver an API with desired PSD at the lab scale These ranges are utilized as guidelines for initial pilot scale execution. The PSD resulting from the plant scale runs can differ from that generated at the lab scale, even when the runs are conducted using otherwise similar processes with linear scaling of most parameters This can be due to different mixing time scales, equipment configurations, and hydrodynamics, all of which can impact the kinetics of many crystallization mechanisms. PBM is employed to help understand the crystallization mechanisms, and to probe processing parameters that are impactful on final PSD This understanding is utilized to guide lab-scale experiments, to ensure that the processing parameters that influence final PSD are explored appropriately. The general workflow that was utilized to generate the PBM is described here, along with model outputs, with the hopes of seeing PBM more readily adopted into the pharmaceutical workflow
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