(457g) Developing a Systematic Design Approach to Tailor Crystal Size Distribution for Mixing-Sensitive Crystallization Processes

In the pharmaceutical industry, both company internal and regulatory authorities impose stringent requirements on the product quality, which includes crystal size distribution, of active pharmaceutical ingredients (APIs) obtained from crystallization processes [1]. In addition, the development of the crystallization process for a given API includes the design of control strategies to ensure the crystal product meet the demands of the drug administration method and the bioavailability, as well as the required physical attributes for the efficiency of downstream processes (e.g. filtration and drying) [2-4]. In particular, a narrow particle size distribution is especially important for inhalation drugs, in which the specific size range would depend on the region of the human respiratory tract where the drug is targeted [5, 6].

The design of crystallization processes becomes more complicated if mixing has an effect on the final crystal product quality (e.g., crystal size distribution and polymorphic form). Such mixing effects are more apparent in antisolvent and reactive crystallizations, which involve the blending of different fluids, and in large-scale crystallizers, where homogeneity cannot be easily achieved. Hence, it is necessary to develop tools to understand the interactions between hydrodynamics and the kinetics of crystallization in order to develop appropriate design methodologies.

The numerical modeling of reactive crystallization that incorporates the effects of macromixing and micromixing have been widely studied for different crystallizer geometries [7-10]. Recently, these modeling approaches were extended to simulate supercritical antisolvent crystallization systems [11].

In this poster, we present the use of an integrated algorithm, which couples macromixing and micromixing models with the population balance equation, to model antisolvent crystallization in a stirred vessel [12] and impinging jet crystallizers. We investigate the dependency of the crystal size distribution on the mixing speed and scale for a stirred vessel, and the effects of jet velocity on the crystal size distribution and polymorphic form for an impinging jet crystallizer.

The goal of such computational tools is to enable the numerical determination of the crystal size distribution and polymorphic form for a wide range of operating conditions for a given set of crystallizer designs and control schemes. Subsequently, the mixer(s), vessel internal design, and operating conditions which result in the desired crystal size distribution and polymorph form would be determined. This systematic design approach would especially be useful for scale-up [13], where the product quality must be maintained at the industrial scale. In addition, the use of numerical simulations to design crystallization processes would significantly reduce the amount of API required for experiments. Nevertheless, the development of this design strategy still faces major challenges. Issues regarding the determination of hydrodynamics-independent crystallization kinetics, the estimation of parameters in the macromixing and micromixing models, and the complete validation of the computational model are discussed.

References

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12. Woo, X.Y., R.B.H. Tan, P.S. Chow, and R.D. Braatz, Simulation of mixing effects in antisolvent crystallization using a coupled CFD-PDF-PBE approach. Crystal Growth & Design, 2006. In Press.

13. Marchisio, D.L., L. Rivautella, and A.A. Barresi, Design and scale-up of chemical reactors for nanoparticle precipitation. AIChE Journal, 2006. 52(5), 1877-1887.