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Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration

Lookup NU author(s): Dr Mike Probert

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Abstract

We combine state-of-the-art computational crystal structure prediction (CSP) techniques with a wide range of experimental 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 obtaining polymorphs of the well-known pharmaceutical isoniazid and show that CSP provides the structure of the recently obtained, but unsolved, Form III of this drug despite there being only a single resolved 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 nonsolvated 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 computational-experimental approach to “de-risk” solid form landscapes.


Publication metadata

Author(s): Taylor CR, Mulvee MT, Perenyi DS, Probert MR, Day GM, Steed JW

Publication type: Article

Publication status: Published

Journal: Journal of the American Chemical Society

Year: 2020

Volume: 142

Issue: 39

Pages: 16668-16680

Print publication date: 30/09/2020

Online publication date: 08/09/2020

Acceptance date: 08/09/2020

Date deposited: 13/11/2020

ISSN (print): 0002-7863

ISSN (electronic): 1520-5126

Publisher: American Chemical Society

URL: https://doi.org/10.1021/jacs.0c06749

DOI: 10.1021/jacs.0c06749


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