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

Hodgkinson, Paul (Durham University, UK); Bourne, Esther (Durham University, UK); Guest, Jamie (Durham University, UK)

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The essential synergy of MD simulation and NMR in understanding amorphous drug forms [dataset] Open Access

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 molecules are observed to be highly dynamic well below the glass transition, and averaging over this dynamics is essential to understanding the observed NMR shifts. Predicted linewidths are consistently about 2 ppm narrower than observed experimentally, which is hypothesised to 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.

Descriptions

Resource type: Dataset
Contributors: Editor: Hodgkinson, Paul ¹
Creator: Bourne, Esther ¹
Creator: Guest, Jamie ¹

¹ Durham University, UK
Funder: Engineering and Physical Sciences Research Council
Research methods
Other description
Keyword: Solid state
Amorphous pharmaceuticals
NMR
Subject: Nuclear magnetic resonance spectroscopy
Amorphous substances
Location
Language
Cited in
Identifier: ark:/32150/r2m900nt43g
doi:10.15128/r2m900nt43g
Rights: Creative Commons Attribution 4.0 International (CC BY)
Publisher: Durham University
Date Created

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Depositor: P. Hodgkinson
Date Uploaded: 14 June 2024, 09:06:36
Date Modified: 15 June 2024, 08:06:58
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Last modified: 2024:06:14 10:02:08+01:00

Filename: Amorphous Data.zip

Original checksum: caecc0a8c0b7efae1d656495b8f046e3

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