Modeling the Influence of Fatty Acid Incorporation on Mesophase Formation in Amphiphilic Therapeutic Delivery Systems

Dispersed amphiphile-fatty acid systems are of great interest in drug delivery and gene therapies because of their potential for triggered release of their payload. The mesophase behavior of these systems is extremely complex and is affected by environmental factors such as drug loading, percentage and nature of incorporated fatty acids, temperature, pH, and so forth. It is important to study phase behavior of amphiphilic materials as the mesophases directly influence the release rate of the incorporated drugs. We describe a robust machine learning method for predicting the phase behavior of these systems. We have developed models for each mesophase that simultaneous and reliably model the effects of amphiphile and fatty acid structure, concentration, and temperature and that make accurate predictions of these mesophases for conditions not used to train the models.两亲物（amphiphile）-脂肪酸分散体系在药物递送与基因治疗领域备受关注，因其具备触发释放有效载荷（payload）的潜力。这类体系的中间相行为（mesophase behavior）极为复杂，受载药量、掺入脂肪酸的占比与性质、温度、pH值等多种环境因素影响。研究两亲材料的相行为至关重要，因为中间相直接影响所负载药物的释放速率。本研究提出一种鲁棒的机器学习方法，用于预测这类体系的相行为。我们针对每种中间相开发了相应模型，可同时且可靠地建模两亲物与脂肪酸的结构、浓度及温度的影响，并能针对未纳入模型训练的实验条件，准确预测这类中间相的形成情况。