Application of Crystal Engineering in Multicomponent Pharmaceutical Crystals: A Study of Theory and Practice
Abstract
Multicomponent crystallization, a prominent strategy in crystal engineering, offers the ability to modify the physicochemical properties of crystals by introducing a secondary component to their lattice structure. Such multicomponent crystals have found widespread application in the pharmaceutical industry. This thesis explores the experimental screening, characterization, application, and theoretical prediction of multicomponent crystals of Active Pharmaceutical Ingredients (APIs).
The first case study investigates a new solvate of Dasatinib which exhibits high instability at room temperature and transforms into a different polymorph upon desolvation. The crystal structure of this compound is obtained, revealing insights into its transient nature and the potential application of desolvation for particle size reduction.
Another case study focuses on synthesizing a new cocrystal of zinc-phenylacetate (Zn-PA) with isonicotinamide (INAM). The resulting Zn-PA-INAM ionic cocrystal resolves the hydrophobicity issue of Zn-PA, enhancing solubility and dissolution rate. The crystal structure of Zn-PA-INAM, lattice energy comparison, and crystal morphology studies provide scientific explanations for these alterations.
Additionally, this thesis proposes computational prediction strategies to discover new multicomponent crystals. Quantitative predictive approaches based on hydrogen bonding strength are investigated, employing DFT-derived electrostatic potential (ESP) maps, hydrogen bond energy (HBE) and propensity (HBP) calculations. We demonstrate the enhanced classification capability achieved by combining HBE and HBP through multivariate logistic regression.
Expanding on cocrystal prediction strategies, we performed DFT calculations for a comprehensive database of 6,388 cocrystals from literature reports of both successful and unsuccessful experimental attempts. The extracted ESP surfaces were utilized to develop robust machine learning models that demonstrated exceptional discriminatory performance and achieved up to 94% accuracy on unseen test data.
Lastly, an investigation is conducted on the crystal morphology of Rufinamide (RUF), utilizing temperature cycling, solvent screening, and additive selection to modify its thread-like morphology into a more isometric shape. The crystal structures of three RUF polymorphs are determined, and a connection between the microscopic structure and the macroscopic morphologies is established through face indexing.
This thesis provides valuable insights into the application and systematic discovery of multicomponent crystals. By combining experimental screening, characterization, and predictive tools, it contributes to advancing the field’s understanding and utilization of multicomponent crystals.