The essential synergy of MD simulation and NMR in understanding amorphous drug forms

Guest, Jamie L.; Bourne, Esther A.E.; Screen, Martin A.; Wilson, Mark R.; Pham, Tran N.; Hodgkinson, Paul

Abstract

Molecular dynamics (MD) simulations and chemical shifts from machine learning are used to predict 15N, 13C and 1H chemical shifts for the amorphous form of the drug irbesartan. The local environments are observed to be highly dynamic well below the glass transition, and averaging over the dynamics is essential to understanding the observed NMR shifts. Predicted linewidths are about 2 ppm narrower than observed experimentally, which is hypothesised to largely result from susceptibility effects. Previously observed differences in the 13C shifts associated with the two tetrazole tautomers can be rationalised in terms of differing conformational dynamics associated with the presence of an intramolecular interaction in one tautomer. 1H shifts associated with hydrogen bonding can also be rationalised in terms of differing average frequencies of transient hydrogen bonding interactions.

Citation

Guest, J. L., Bourne, E. A., Screen, M. A., Wilson, M. R., Pham, T. N., & Hodgkinson, P. (online). The essential synergy of MD simulation and NMR in understanding amorphous drug forms. Faraday Discussions, https://doi.org/10.1039/d4fd00097h

• Journal Article Type: Article
• Acceptance Date: Jun 20, 2024
• Online Publication Date: Jun 20, 2024
• Deposit Date: Aug 2, 2024
• Publicly Available Date: Aug 2, 2024
• Journal: Faraday Discussions
• Print ISSN: 1359-6640
• Electronic ISSN: 1364-5498
• Publisher: Royal Society of Chemistry
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1039/d4fd00097h
• Public URL: https://durham-repository.worktribe.com/output/2736775