Daria Torodii^1*, Jacob Holmes^1,2, Pinelopi Moutzouri¹, Sten Nilsson Lill³, Manuel Cordova^1,2, Arthur Pinon⁴, Kristof Grohe⁵, Sebastian Wegner⁵, Okky Dwichandra Putra⁶, Stefan Norberg⁷, Anette Welinder⁷, Staffan Schantz⁷, Lyndon Emsley^1,2*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

2 National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

3 Data Science & Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, 43183 Gothenburg, Sweden

4 Swedish NMR Center, Department of Chemistry and Molecular Biology, University of Gothenburg, 41390 Gothenburg, Sweden

5 Bruker BioSpin GmbH & Co KG, 76275 Ettlingen, Germany

6 Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca, 43183 Gothenburg, Sweden

7 Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, 43183 Gothenburg, Sweden

* Corresponding authors emails: daria.torodii@epfl.ch, lyndon.emsley@epfl.ch

DOI10.24435/materialscloud:qk-x9 [version v1]

Publication date: Sep 17, 2024

How to cite this record

Daria Torodii, Jacob Holmes, Pinelopi Moutzouri, Sten Nilsson Lill, Manuel Cordova, Arthur Pinon, Kristof Grohe, Sebastian Wegner, Okky Dwichandra Putra, Stefan Norberg, Anette Welinder, Staffan Schantz, Lyndon Emsley, Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning, Materials Cloud Archive 2024.136 (2024), https://doi.org/10.24435/materialscloud:qk-x9

Description

The recent development of ultra-fast MAS (>100 kHz) provides new opportunities for structural characterization in solids. Here we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1H resolution improvement at ultra-fast MAS and use solely 1H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13C-detected experiments, without the need to employ dynamic nuclear polarization.

Materials Cloud sections using this data

Files

File name	Size	Description
verinurad_TopSpin_data.zip MD5md5:179369f56a39d2f1c32c8a98e9d18047	487.4 MiB	Raw NMR data of the experimental spectra measured.
verinurad_pip_TopSpin_data.zip MD5md5:e4b7342c4f2caddd1a98a8ffeaf4f69a	3.3 MiB	Raw NMR data of the experimental spectra measured and used as input for PIPNet.
Probabilistic_assignment.zip MD5md5:11db59eb753daa27c3522025da5f8e1a	1.0 MiB	The input files used for the Bayesian probabilistic assignment and the complete output data generated
ShiftML2_predictions.zip MD5md5:b3bd8089ea7d0eb8b31a1d5a32656ce3	27.4 KiB	SXRD structure of verinurad with proton relaxed positions used as input for ShiftML2 and the Excel sheet showing the chemical shielding to chemical shift conversion
GIPAW_calculations.zip MD5md5:2ec088fd922d934d39462c55dfc47b33	406.6 KiB	Input/output files for proton optimization and NMR calculation

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Solid-State NMR NMR crystallography Verinurad