Crystallography of Pharmaceuticals: Ways to improve efficiency and assessing the fragment-based approach for enhanced polymorph discrimination

Nuclear magnetic resonance (NMR) crystallography is an emerging method that combines experimental and computational data to perform various characterization tasks, such as structure determination, validation, and refinement. NMR crystallography can be used to determine the crystal structures of chemicals even when other experimental methods, such as X-ray diffraction, cannot. Here, we discuss aspects of computational modeling at the Density Functional Theory (DFT) level. Specifically, we calculated NMR parameters such as magnetic shielding values. Most often, we will be looking at 1H magnetic shielding values, as they are associated with the hydrogen atoms that are regularly involved in the intermolecular interactions needed to form crystalline structures. On occasion, we will also consider 13C magnetic shielding values. With this in mind, we focussed on how we can efficiently determine the crystal structures of small molecule organics at the DFT level but using less computational time and resources. We have considered four organic systems: cocaine, flutamide, AZD8329, and flufenamic acid. After, we will briefly discuss the fragment-based approach for calculating NMR parameters and how this approach allows us to include relativistic effects at the spin-orbit level of theory, which we hope can enhance our ability to distinguish between different polymorphs of a chemical. In this portion of the thesis, we studied three organics: sulfanilamide, theophylline, and cocaine.

Name thesis:degree_name

Master of Science (MSc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Chemistry

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2023

Author dc:creator

• Kaur, Navjot

Advisor dc:contributor.advisor

• Widdifield, Cory

Committee member dc:contributor.committeemember

• Murphy, R. Scott

Language dc:language.iso

en

DOI

https://doi.org/10.82465/4930

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/16467