(385g) A Systematic Approach Using Model-Based Digital Design for Polymorphic Crystallization of Imatinib Mesylate

Polymorphism is prevalent and important in pharmaceutical manufacturing. It is a challenging issue during crystallization as it significantly affects the properties of active pharmaceutical ingredients (API). Different polymorphs of the same API can have different bioavailability, stability, solubility, and manufacturability (Simone et al., 2017). Process modeling and digital design by optimization help accelerate design space evaluation by reducing the required number of experiments and costs (Diab et al., 2019). However, parameter estimation and modeling of polymorphic transformation bring additional complexity to the development of a digital twin as the models require to capture of additional mechanisms such as dissolution of the metastable form and nucleation and growth of the stable form (Cardew & Davey, 1985; Ono et al., 2004; Kshirsagar et al., 2023). This work aims to define a systematic approach for developing a polymorphic crystallization model of a chemotherapeutic agent, Imatinib Mesylate, which is used to treat certain types of leukemia and gastrointestinal stromal tumors (Lin et al., 2019).

In the present work, isothermal antisolvent crystallization experiments are demonstrated to show the potential of selective crystallization for the two polymorphs of Imatinib Mesylate: the needle-shaped metastable α-form and the cubic-shaped stable β-form. The polymorphic transformation of imatinib mesylate is investigated using a systematic experimental design together with population balance modeling. The kinetics of isothermal antisolvent crystallization of α-form and β-form imatinib mesylate are determined for the first time. The solvent-antisolvent (methanol-isopropanol) ratio affects the dissolution and growth rates of different forms. In order to have more yield, the minimum solvent composition used in the experiments is chosen to be 50% methanol-50% isopropanol by volume. The implementation of appropriate feedback control approaches using process analytical technology (PAT) tools enable the control of the polymorphic transformation and the growth of both metastable and stable polymorphs (Simone et al., 2017). In situ Raman spectroscopy is used to detect the polymorphs of Imatinib Mesylate in suspension. ATR-UV/Vis and ATR-FTIR are used to monitor and measure the solute concentration along with offline UPLC measurements for confirmation. EasyViewer (inline particle size analyzer) is used for monitoring the number of counts and chord length distribution of the crystals. The polymorphic form of the product and mean crystal size are chosen as critical quality attributes (CQAs). Isothermal seeded and unseeded crystallization experiments are performed in the design of experiments (DoE) by considering the antisolvent addition rate and seed loading as factors. This study aims to reduce experimental efforts and resources by utilizing the validated polymorphic model for the in-silico design of experiments to study the effect of various process parameters on product quality. It demonstrates the potential of model-based digital design in rapid process development for polymorphic crystallization. With the gathered data, a digital design of the system is produced to use model-based control methods to find the polymorphic design spaces of α- and β-forms. The results yield a feasible design space for the crystallization of the desired stable β-form.

Acknowledgment:

This work is supported by the US Food and Drug Administration (FDA) under contract number 75F40121C00106 and by the National Science Foundation (NSF) EFRI-DChEM award number 2132142.

References:

Cardew, P. T., & Davey, R. J. (1985). The kinetics of solvent-mediated phase transformations. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 398(1815), 415–428. https://doi.org/10.1098/rspa.1985.0043

Diab, S., Mytis, N., Boudouvis, A. G., & Gerogiorgis, D. I. (2019). Process modelling, design and Technoeconomic Liquid–Liquid Extraction (LLE) optimisation for comparative evaluation of batch vs. continuous pharmaceutical manufacturing of atropine. Computers & Chemical Engineering, 124, 28–42. https://doi.org/10.1016/j.compchemeng.2018.12.028

Kshirsagar, S., Szilagyi, B., & Nagy, Z. K. (2023). Experimental design for the efficient determination of the crystallization kinetics of a polymorphic system in combined cooling and antisolvent crystallization. Crystal Growth & Design, 23(3), 1486–1499. https://doi.org/10.1021/acs.cgd.2c01075

Lin, M., Wu, Y., & Rohani, S. (2019). A kinetic study of crystallization process of imatinib mesylate with polymorphic transformation phenomenon. Journal of Crystal Growth, 507, 146–153. https://doi.org/10.1016/j.jcrysgro.2018.10.061

Ono, T., Kramer, H. J., ter Horst, J. H., & Jansens, P. J. (2004). Process modeling of the polymorphic transformation of l-glutamic acid. Crystal Growth & Design, 4(6), 1161–1167. https://doi.org/10.1021/cg0497694

Simone, E., Szilagyi, B., & Nagy, Z. K. (2017). Systematic model identification and optimization-based active polymorphic control of crystallization processes. Chemical Engineering Science, 174, 374–386. https://doi.org/10.1016/j.ces.2017.09.034