The essential synergy of MD simulation and NMR in understanding amorphous drug forms [dataset]

Molecular dynamics (MD) simulations and chemical shifts from machine learning are used to predict 15N, 13C and 1H chemical shifts for the amorphous form of the drug irbesartan. The molecules are observed to be highly dynamic well below the glass transition, and averaging over this dynamics is essential to understanding the observed NMR shifts. Predicted linewidths are consistently about 2 ppm narrower than observed experimentally, which is hypothesised to result from susceptibility effects. Previously observed differences in the 13C shifts associated with the two tetrazole tautomers can be rationalised in terms of differing conformational dynamics associated with the presence of an intramolecular interaction in one tautomer. 1H shifts associated with hydrogen bonding can also be rationalised in terms of differing average frequencies of transient hydrogen bonding interactions.

本研究结合分子动力学（Molecular Dynamics, MD）模拟结果与机器学习生成的化学位移数据，对药物厄贝沙坦（irbesartan）无定形态的15N、13C及1H化学位移进行预测。研究观测到，在远低于玻璃化转变温度的条件下，该药物分子呈现出显著的动态特性，而对该动态过程进行时间平均处理，是解析实验观测到的核磁共振（Nuclear Magnetic Resonance, NMR）位移的必要前提。预测得到的谱线线宽始终比实验观测值窄约2 ppm，该现象被假设为磁化率效应所致。此前观测到的两种四唑（tetrazole）互变异构体（tautomers）对应的13C化学位移差异，可通过其中一种互变异构体存在分子内相互作用（intramolecular interaction）所引发的构象动态差异得到合理解释。与氢键作用相关的1H化学位移，也可通过瞬态氢键相互作用的平均频率差异得到合理解释。