Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration.

Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration.
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通过合作计算和实验探索最小化多态风险。

DOI:
10.1021/jacs.0c06749
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发表时间:
2020-09-30
影响因子:
15
通讯作者:
Steed JW
Steed JW
中科院分区:
化学1区
文献类型:
--
作者:
Taylor CR;Mulvee MT;Perenyi DS;Probert MR;Day GM;Steed JW

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我们将联合收割机最先进的计算晶体结构预测(CSP)技术与广泛的实验结晶方法相结合,以了解和探索药物中的晶体结构,并最大限度地降低意外后期出现多晶型的风险。最初,我们证明了CSP的力量,以合理化的困难,在获得多晶型的著名的药物异烟肼,并显示CSP提供的结构,最近获得的,但未解决的,形式III的这种药物,尽管只有一个单一的解决形式近70年。更戏剧性的是,我们的盲法CSP研究预测了相关的异丙肼多态性的显著风险。采用各种各样的实验技术,包括高压实验,我们实验获得了前三个已知的非溶剂化结晶形式的异丙烟肼,所有这些都成功地预测在CSP程序。我们展示了CSP方法和自由能计算的力量,以合理化观察到的第三种形式的异丙烟肼,在获得它的高压实验的成功,以及我们的协同计算实验方法的能力,以“去风险”固体形式的景观。
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.
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