Optimal Operation of a Seeded Pharmaceutical Crystallization with Growth-Dependent Dispersion

Abstract

A cooling profile for a seeded pharmaceutical crystallization designed to achieve a desired mean crystal size is found by minimizing the batch operating time subject to a crystallization model. In many systems, optimizing a characteristic of the product crystal size density is the usual objective. This objective is not feasible for this system because growth-dependent dispersion occurs in the absence of secondary nucleation. Instead, the batch operating time is minimized, and the resulting characteristics of the crystal size density are then fixed by the operating policy. To prevent undesired secondary nucleation, both a cooling-rate constraint and a supersaturation constraint are investigated. The optimal policies are implemented and verified experimentally. The experimental measurements include solution concentration, slurry transmittance, and video microscopy. The video microscopy is used to determine the mean crystal size and standard deviation. These values are found to agree closely with the model predictions for the optimal operating policies. In addition to determining the optimal operating policies, this study is the first to use video images for monitoring crystal size density mean and standard deviation as part of crystallizer model validation.

Continuous Plug Flow Crystallization of Pharmaceutical Compounds

“…53 Growth rate dispersion implies that crystals of the same size and shape grow at different rates in the same environment. [54][55][56][57] Two different mechanisms that lead to growth rate dispersion have been proposed and have experimental support. 53 The random fluctuations mechanism assumes that all crystals have the same time-averaged growth rate but that individual crystals growth rates fluctuate during growth periods.…”

The potential of current high‐resolution imaging‐based particle size distribution measurements for crystallization monitoring

“…Only a few studies have used video imaging for on‐line, quantitative monitoring of crystal population dynamics. Patience et al4 used video microscopy to monitor the evolution of crystal size mean and standard deviation for needle‐like pharmaceutical particles. The slurry was sampled periodically and allowed to settle on a microscope stage, and the images were analyzed manually.…”

A combined approach of differential scanning calorimetry and hot-stage microscopy with image analysis in the investigation of sulfathiazole polymorphism

“…The introduction of an alternative way of presenting HSM results may broaden the application of HSM since it is conventionally used to confirm results of other thermal analysis techniques only. Some visual techniques, including optical microscopy, non-invasive video imaging systems and Lasentec's inprocess video microscopy, have successfully utilized image analysis in the investigation of the dynamics of product quality, such as size and shape of particulates in real-time and in situ [15][16][17].…”