Jing J. Liu

Effect of seed loading and cooling rate on crystal size and shape distributions in protein crystallization—A study using morphological population balance simulation

Abstract A morphological population balance model is applied to crystallization of hen-egg white lysozyme for investigation of the effect of seed loading and cooling rate on supersaturation, and crystal size and shape distributions. Growth rates of individual faces and final crystal size and shape distributions were examined under varied seeding and cooling conditions. It was found that for growth only crystallization, desired crystal size and shape can be obtained by coordinative manipulation of the seed loading and cooling rate: low seed loading and high cooling rate lead to large crystals of low aspect ratio, but care has to be taken to avoid nucleation and major shape change such as width becoming larger than the length. The interesting results not only demonstrate the effectiveness of morphological population balance simulation for protein crystallization but also provide useful knowledge for process optimization and control.

Optimization and control of crystal shape and size in protein crystallization process

Abstract Large molecule protein crystals have shown significant benefits in the delivery of biopharmaceuticals to achieve high stability, high concentration of active pharmaceutical ingredients (API), and controlled release of API. However, among the about 150 biopharmaceuticals on the market by 2004, only insulin has been marketed in crystalline form. A major technological challenge is that protein crystallization has a very complicated environment and is affected by many factors. There is currently a lack of knowledge on large scale production of protein crystals. In contrast to the majority of previous work on protein crystallization that was centered on single crystal scale, the current research is focused on computational study of protein crystallization at process scale, investigating the growth behavior of a population of crystals in a crystallizer. Using a newly developed morphological population balance model that can simulate the multidimensional size distributions of a population of crystals, known as shape distribution, an optimization technique is applied to optimize the growth of individual faces with the aim of obtaining desired crystal shape and size distributions. Using a target shape as the objective function, optimal temperature and supersaturation profiles leading to the desired crystal shape were derived. Genetic algorithm was investigated and found to be an effective optimization technique for the current application. Since tracking an optimum temperature or supersaturation trajectory can be easily implemented by manipulating the coolant flowrate in the reactor jacket, the methodology provides a feasible closed-loop mechanism for protein crystal shape tailoring and control.

Morphological Population Balance Models for the Dynamic Evolution of Particle Shape and Size Distribution in Protein Crystallization

Abstract Protein crystallization is known to be affected by many factors and inherently difficult to control. Being able to model the crystal growth, especially at process scale for the population of particles in a reactor rather than for a single particle, will no doubt greatly help the formulation and manufacture of protein crystals. In this paper, a morphological population balance model is presented which has incorporated the crystal shape information into the population balance process model therefore is able to simultaneously simulate the dynamic evolution of shape as well as size for crystals of tetragonal Hen-Egg-White (HEW) lysozyme within a crystallizer. Morphological population balance models require growth kinetics data for each facet, which was obtained from published data in literature for the two identified independent crystallographic faces, {101} and {110}, of HEW lysozyme.

A multi-component mass transfer rate based model for simulation of non-equilibrium growth of crystals

A model based on multi-component mass transfer is proposed for modeling the non-equilibrium growth behavior of crystals during solution crystallization. The multi-component composition in crystals in any spatial location can thus be estimated at any time during a crystallization process. It can be applied to the estimation of impurity content and assessing the stability of crystalline pharmaceuticals. The multi-components are equally described by diffusion, adsorption and integration equations. The facet growth rates are estimated by the amount of materials grown on the surface divided by the material densities and the surface areas. This is unlike the conventional facet growth kinetic model in which the growth rate is correlated directly to supersaturation. The modeling method is illustrated by case studies of NaNO3 and KDP crystallization. The dynamic evolution of crystal composition and shape distribution is simulated.