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

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.