Jeliński Tomasz, Przybyłek Maciej, Różalski Rafał, Romanek Karolina, Wielewski Daniel, Cysewski Piotr
Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-096 Bydgoszcz, Poland.
Department of Clinical Biochemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Karłowicza 24, 85-950 Bydgoszcz, Poland.
Molecules. 2024 Aug 13;29(16):3841. doi: 10.3390/molecules29163841.
Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the C28 type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems.
低共熔溶剂（DES）是一类很有前景的绿色溶剂，在天然化合物如阿魏酸（FA）的提取和新制剂开发中具有特殊用途。该研究的实验阶段对FA在DES中的溶解度进行了系统研究，DES包括以氯化胆碱或甜菜碱作为氢键受体以及六种不同的多元醇作为氢键供体。结果表明，基于氯化胆碱的溶剂比基于甜菜碱的溶剂更有效。发现氢键受体与供体的最佳摩尔比为1:2。向DES中加水会显著提高FA的溶解度。在所测试的多元醇中，三甘醇最有效。因此，建议使用由氯化胆碱和三甘醇（TEG）（1:2）组成并添加0.3摩尔比水的DES作为二甲基亚砜等传统有机溶剂的有效替代品。在本报告的第二部分，计算了饱和溶液中FA对于溶质 - 溶质和所有溶质 - 溶剂对的亲和力。发现FA的自缔合会导致C28型环状结构，这在羧酸中很常见，是FA亲和力最强的类型。另一方面，在所有异分子双配合物中，最稳定的是FA - TEG对，这与FA在含TEG液体中的高溶解度具有有趣的一致性。最后，这项工作将COSMO - RS建模与机器学习相结合，以开发一个预测阿魏酸在多种溶剂中溶解度的模型，这些溶剂不仅包括DES，还包括经典的纯溶剂和二元混合物。一种机器学习协议开发了一个用于预测FA溶解度的高精度模型，显著优于COSMO - RS方法。基于所得结果，建议使用支持向量回归器（SVR）来筛选新的溶解介质，因为它不仅准确，而且对新系统具有良好的泛化能力。
