Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models,Journal of Computational Chemistry

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Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models
Journal of Computational Chemistry ( IF 3.4 ) Pub Date : 2024-07-03 , DOI: 10.1002/jcc.27454
Robert Fox ₁ , Joaquin Klug ₂ , Damien Thompson ₃ , Anthony Reilly ₁

Affiliation

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4′-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.

中文翻译：

共晶形成的计算预测：28 个组件的基准研究，比较从高通量到高级模型的五种方法

共晶体是通过非共价相互作用稳定的超过一种类型的分子的组装体。它们是用于改进药物配方的有前途的材料，其中通过包含共形成物来改善活性药物成分（API）的稳定性、溶解度或生物相容性。在这项工作中，系统地比较了一系列密度泛函理论 (DFT) 和密度泛函紧密结合 (DFTB) 模型预测各种现有药物相关共晶的晶格焓的能力。这些范围从含有模型化合物 4,4'-联吡啶和草酸的共晶体到具有良好基准的阿司匹林和扑热息痛 API 的共晶体，所有这些都经过大量替代共形成物的测试。对于简单共晶，不同 DFT 模型计算的晶格焓存在普遍共识。对于具有 API 共形成物的共晶，焓预测强烈依赖于 DFT 模型。即使对于简单的共晶，明显更轻的 DFTB 模型也可以预测不切实际的晶格焓值。

更新日期：2024-07-03

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