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Structure determination of an amorphous drug through large-scale NMR predictions

Manuel Cordova1*, Martins Balodis1, Albert Hofstetter1, Federico Paruzzo1, Sten O. Nilsson Lill2, Emma S. E. Eriksson2, Pierrick Berruyer1, Bruno Simões de Almeida1, Michael J. Quayle3, Stefan T. Norberg4, Anna Svensk Ankarberg4, Staffan Schantz4*, Lyndon Emsley1*

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

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

3 New Modalities and Parenteral Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden

4 Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden

* Corresponding authors emails: manuel.cordova@epfl.ch, Staffan.Schantz@astrazeneca.com, lyndon.emsley@epfl.ch
DOI10.24435/materialscloud:gg-mx [version v1]

Publication date: Mar 16, 2021

How to cite this record

Manuel Cordova, Martins Balodis, Albert Hofstetter, Federico Paruzzo, Sten O. Nilsson Lill, Emma S. E. Eriksson, Pierrick Berruyer, Bruno Simões de Almeida, Michael J. Quayle, Stefan T. Norberg, Anna Svensk Ankarberg, Staffan Schantz, Lyndon Emsley, Structure determination of an amorphous drug through large-scale NMR predictions, Materials Cloud Archive 2021.41 (2021), doi: 10.24435/materialscloud:gg-mx.


Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state NMR is among the most popular methods to characterize amorphous materials, and Molecular Dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.

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File name Size Description
10.2 KiB Detailed description of the files in the dataset
1.3 MiB Python notebooks used for data analysis
350.8 KiB Figures generated by the Python scripts
444.9 MiB Raw NMR data
46.8 KiB CIF files of candidate crystal structures, XRD structure, potential tautomers, and perturbed crystal structure.
1.7 MiB Chemical shift computation of the candidate crystal structures
2.7 GiB Molecular Dynamics trajectories of amorphous AZD5718 with different water contents
2.3 GiB Predicted shieldings for all snapshots of the MD trajectories
1.3 MiB Formation energies of molecules in the MD trajectories


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

Journal reference (Paper where the data is discussed)
M. Cordova, M. Balodis, A. Hofstetter, F. Paruzzo, S. O. Nilsson Lill, E. S. E. Eriksson, P. Berruyer, B. Simões de Almeida, M. J. Quayle, S. T. Norberg, A. Svensk Ankarberg, S. Schantz, L. Emsley, (submitted)


MARVEL/DD1 SNSF machine learning molecular dynamics Experimental NMR

Version history:

2021.41 (version v1) [This version] Mar 16, 2021 DOI10.24435/materialscloud:gg-mx