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

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 () and Lyndon Emsley ()
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Manuel Cordova: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)
Martins Balodis: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)
Albert Hofstetter: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)
Federico Paruzzo: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)
Sten O. Nilsson Lill: Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca
Emma S. E. Eriksson: Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca
Pierrick Berruyer: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)
Bruno Simões de Almeida: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)
Michael J. Quayle: New Modalities and Parenteral Development, Pharmaceutical Technology & Development, Operations, AstraZeneca
Stefan T. Norberg: Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca
Anna Svensk Ankarberg: Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca
Staffan Schantz: Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca
Lyndon Emsley: Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract 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 nuclear magnetic resonance (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.

Date: 2021
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DOI: 10.1038/s41467-021-23208-7

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