(310e) A Multiscale Computational Method for Prediction of Polymorphs

Physical properties of solid pharmaceutical drugs stem from their crystalline structure or polymorph. Pharmaceutical molecules can undergo polymorphism to attain more than one crystalline structure depending on the crystallization conditions. Experimental screening of polymorphs is expensive and time-consuming. Therefore, the accurate methods to predict polymorphs have become essential to build in quality into the pharmaceutical products. The most prominent method to predict crystalline polymorph is the lattice energy minimization technique, which uses appropriate sampling and optimization algorithms to identify space groups and lattice energy. This approach has limitations in treating the solute-solvent interactions and capturing the effect of additives, solvents, co-solvents and hydrodynamic conditions of the crystallizer. Therefore, there is a need to develop a model which considers these effects involved in multiple length and time scales during the crystallization process. Here we propose a consistent multiscale approach to predict dynamics of polymorph formation, which includes hierarchical simulation of sequential events – i) lattice formation via self-assembly of molecules, ii) growth of lattice and formation of molecular clusters, and iii) development of faces and evolution of faceted crystal. The effect of the solute-solvent interactions, additives, solvents, co-solvents and hydrodynamic conditions are embedded into the calculation of free energy barriers for molecular attachment/detachment in these three events. An example of polymorphism in Glutamic acid crystals using the multiscale method will be shown.