(755d) Protein Crystal Shape and Size Control Using a Plug Flow Crystallization Configuration

Authors: 
Kwon, S., UCLA
Nayhouse, M., UCLA
Ni, D., Chinese Academy of Science
Orkoulas, G., University of California, Los Angeles
Christofides, P. D., University of California, Los Angeles

Crystallization plays a key role in the context of separation and purification methods for the production of therapeutic drugs. Considering the fact that crystal size and shape distributions have significant influence on the bioavailability of drugs such as the dissolution rate, filterability in the subsequent downstream process, and stability as a carrier to the target site, it becomes of particular interest to the pharmaceutical industry to produce crystals with desired size and shape distributions [1]-[2]. Traditionally, batch crystallization processes have been widely used in the pharmaceutical industry. However, owing to a few well-known potential drawbacks OF batch processes such as batch-to-batch variability, the difficulty in the scale-up,   mixed suspension mixed product removal (MSMPR) crystallization process has received   growing attention, and many efforts have been made in order to produce crystals from the MSMPR process with a higher production rate and desired product quality [3]-[4]. However, due to the presence of back-mixing, those crystals nucleated at a later stage during the crystallization process will reside a relatively short amount of time in the crystallizer and thus they will end up leaving the crystallizer with undesired size and shape distributions. To this end, plug flow crystallizer (PFC) has been proposed to produce crystals with desired size and shape distributions [5]-[7].

Motivated by the above considerations, the present work focuses on modeling of a PFC with 5 segments for the production of tetragonal HEW lysozyme crystals through kinetic Monte Carlo (kMC) simulation methods in the way described in [8] using the rate equations originally developed by [9]. A seeding strategy is used to decouple the nucleation from the crystal growth processes [10]. Furthermore, a constraint on the supersaturation level is imposed so that the system is enforced to stay in the metastable regime where the degree of primary nucleation is negligible [11]. Then, a population balance equation (PBE) is presented to describe the spatio-temporal evolution of the crystal volume distribution, and by applying the method of moments (MOM) to the PBE, a reduced-order moment model is derived because kMC models are not readily available in a closed form. Along with the mass and energy balance equations, the leading moments are used for the estimation of the spatio-temporal evolution of the crystal size and shape distributions in an optimization problem. More specifically, the crystallizer jacket temperatures at each segment and the superficial flow velocity are chosen as the decision variables in the optimization problem and the objective function is to minimize the squared deviation of the average size and shape of crystals produced throughout the PFC. Besides, dynamic models are developed and used for the design of a feed-forward control (FFC) strategy for the production of crystals with desired size and shape distribution properly suppressing the undesired effect caused by disturbances in the inflow solute concentration and crystal fines distribution.

[1] Patience DB, Rawlings JB. Particle shape monitoring and control in crystallization process. AIChE J. 2001;47:2125-2130.

[2] Wang L, Lee MH, Barton J, Hughes L, Odom TW. Shape-control of protein crystals in patterned microwells. J. Am. Chem. Soc. 2008;130:2142-2143.

[3] Griffin DW, Mellichamp DA, Doherty MF. Reducing the mean size of API crystals by continuous manufacturing with product classification and recycle. Chem. Eng. Sci. 2010;65;5770-5780.

[4] Alvarez A, Singh A, Myerson AS. Crystallization of cyclosporine in a multistage continuous MSMPR crystallizer. Crysal Growth & Design. 2011;11:4392-4400.

[5] Eder R, Schmitt E, Grill J, Radl S, Gruber-Woelfler H, Khinast J. Seed loading effects on the mean crystal size of acetylsalicylic acid in a continuous-flow crystallization device. Cryst. Res. Technol. 2011;3:227-237.

[6] Ridder B, Majumder A, Nagy Z. Population balance model based multi-objective optimization of a Multi-Segment Multi-Addition (MSMA) continuous plug flow antisolvent crystallizer. Ind. & Eng. Chem. Res. 2014;53:4387-4397.

[7] Alvarez A, Myerson A. Continuous plug flow crystallization of pharmaceutical compounds. Crysal Growth & Design. 2010;10:2219-2228.

[8] Kwon JS, Nayhouse M, Christofides PD, Orkoulas G. Modeling and control of crystal shape in continuous protein crystallization. Chem. Eng. Sci. 2014;107:47-57.

[9] Durbin SD, Feher G. Simulation of lysozyme crystal growth by the Monte Carlo method. J. Cryst. Growth. 1991;110:41-51.

[10] Liu JJ, Ma CY, Hu YD, Wang XZ. Effect of seed loading and cooling rate on crystal size and shape distributions in protein crystallization - a study using morphological population balance simulation. Comp. & Chem. Eng. 2010;34:1945-1952.

[11] Shi D, Mhaskar P, El-Farra NH, Christofides PD. Predictive control of crystal size distribution in protein crystallization. Nanotechnology. 2005;16:S562-S574.

Topics: