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Minimizing polymorphic risk through cooperative computational and experimental exploration.

Taylor, Christopher R. and Mulvee, Matthew T. and Perenyi, Domonkos S. and Probert, Michael R. and Day, Graeme M. and Steed, Jonathan W. (2020) 'Minimizing polymorphic risk through cooperative computational and experimental exploration.', Journal of the American Chemical Society., 142 (39). pp. 16668-16680.

Abstract

We combine state-of-the-art computational crystal structure prediction (CSP) techniques with a wide range of experi-mental crystallization methods to understand and explore crystal structure in pharmaceuticals and minimize the risk of unanticipated late-appearing polymorphs. Initially, we demonstrate the power of CSP to rationalize the difficulty in ob-taining polymorphs of the well-known pharmaceutical isoniazid and show that CSP provides the structure of the recently discovered, but unsolved, Form III of this drug despite there being only a single known form for almost 70 years. More dramatically, our blind CSP study predicts a significant risk of polymorphism for the related iproniazid. Employing a wide variety of experimental techniques, including high-pressure experiments, we experimentally obtained the first three known non-solvated crystal forms of iproniazid, all of which were successfully predicted in the CSP procedure. We demonstrate the power of CSP methods and free energy calculations to rationalize the observed elusiveness of the third form of iproniazid, the success of high-pressure experiments in obtaining it, and the ability of our synergistic computa-tional-experimental approach to “de-risk” solid form landscapes.

Item Type:Article
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
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Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
Download PDF
(3451Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1021/jacs.0c06749
Publisher statement:This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Date accepted:No date available
Date deposited:18 September 2020
Date of first online publication:08 September 2020
Date first made open access:18 September 2020

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