不同相对湿度下药物/聚合物复杂系统相行为的模型研究及机器学习预测  

Model study and machine learning prediction of phase behavior of drug/polymer complex systems under different relative humidity

作　　者：樊钦习 丁叶薇 宋昱潼 吴昊旻 吉远辉 Qinxi Fan;Yewei Ding;Yutong Song;Haomin Wu;Yuanhui Ji(School of Chemistry and Chemical Engineering,Southeast University,Nanjing 211100,China)

机构地区：[1]东南大学化学化工学院,南京211100

出　　处：《中国科学：化学》2024年第11期2273-2281,共9页SCIENTIA SINICA Chimica

基　　金：国家自然科学基金项目(编号:22278070,21978047)资助项目。

摘　　要：研究不同相对湿度下药物/聚合物复杂系统的相行为对药物制剂的理性设计、制备与储存条件选择的至关重要性.本文首先通过构建汽-液、液-固多相平衡热力学模型和Gordon-Taylor方程系统研究了药物/聚合物在不同相对湿度(RH=0%、60%、75%)下的热力学相行为和玻璃化转变温度.进一步将微扰链统计缔合流体理论模型参数和分子结构描述符与五种不同的机器学习算法相耦合,发展了不同相对湿度下药物/聚合物复杂系统相图预测的新方法.研究结果表明,与热力学模型参数和分子结构描述符相耦合的不同机器学习方法中,随机森林算法性能最佳,不同相对湿度下药物在聚合物中的溶解度和玻璃化转变温度曲线的预测结果决定系数R2分别为0.970和0.992.研究进一步表明,机器学习算法耦合机制模型参数以及分子结构描述符可实现不同相对湿度下药物/聚合物相图的精准预测,有望为药物制剂的理性设计、制备与储存条件的筛选提供有效指导.To rationally design,prepare,and store pharmacological formulations,it is crucial to understand the phase behavior of complicated drug/polymer complexes under varying relative humidity conditions.By creating thermodynamic models of vapor-liquid and liquid-solid multiphase equilibria and Gordon-Taylor equation systems,the thermodynamic phase behavior and glass transition temperature of drug/polymer at different relative humidity(RH=0%,60%,and 75%)were studied in this paper.Combining five distinct machine learning algorithms with the perturbedchain statistical associating fluid theory model parameters and molecular structure descriptors creates a novel approach for predicting the phase diagram of drug/polymer complex systems under different RH.The results demonstrate that the random forest algorithm has the highest prediction accuracy among machine learning techniques combined with thermodynamic model parameters and molecular structure descriptors.The determination coefficients R2 of the test set results of drug solubility in polymer and glass transition temperature curves at different RH were 0.970 and 0.992,respectively.The study further shows that the machine learning algorithm coupled mechanism model parameters and molecular structure descriptors can achieve accurate prediction of drug/polymer phase diagrams under different RH,which is expected to provide effective guidance for rational design of drug preparations,preparation and storage conditions screening.

关 键 词：无定形固体分散体 相对湿度 热力学相图 机器学习 相平衡 

分 类 号：TQ460.1[化学工程—制药化工] TP181[自动化与计算机技术—控制理论与控制工程]