Sugden Isaac J, Braun Doris E, Bowskill David H, Adjiman Claire S, Pantelides Constantinos C
Molecular Systems Engineering Group, Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Institute for Molecular Science and Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
University of Innsbruck, Institute of Pharmacy, Pharmaceutical Technology, Josef-Moeller-Haus, Innrain 52c, A-6020 Innsbruck, Austria.
Cryst Growth Des. 2022 Jul 6;22(7):4513-4527. doi: 10.1021/acs.cgd.2c00433. Epub 2022 Jun 15.
Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API-coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a -aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.
通过共结晶控制活性药物成分（API）固体形态的物理性质是药物产品开发的重要组成部分。然而，很难知道哪些共形成物会与给定的API形成共晶体，并且目前共晶体发现的技术水平涉及到在药物开发的早期阶段进行昂贵、耗时且受API材料限制的实验筛选。我们提出了一种系统的高通量计算方法，主要目的是识别不太可能产生实验可观察到的共晶体、因此只需进行简短实验检查就可以从任何实验研究中排除的API/共形成物对。基于成熟的晶体结构预测（CSP）方法，该方法通过不需要除了对纯API本身进行CSP研究之外的任何昂贵的量子力学计算来提高效率。通过对涉及3种活性药物成分、9种共形成物和1种溶剂的30个潜在1:1多组分体系（共晶体和溶剂化物）进行计算研究，对该方法和假设进行了测试。同时对所有30对体系进行了详细的实验研究，发现了5种新的共晶体（3种API - 共形成物组合、1个多晶型共晶体实例和1个具有不同化学计量比的共晶体）以及一种乌头酸多晶型物。计算方法表明，对于某些API，只需进行简短的实验检查就可以研究所有潜在API/共形成物对中的很大一部分，从而节省大量的实验工作。
