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.

分散型两亲分子（amphiphile）-脂肪酸体系因其具备触发释放所载药物的潜力，在药物递送与基因治疗领域备受关注。该类体系的介观相（mesophase）行为极为复杂，受载药量、掺入脂肪酸的占比与固有性质、温度、pH值等诸多环境因素影响。研究两亲材料的相行为至关重要，因为介观相会直接影响所载药物的释放速率。本研究提出一种稳健的机器学习方法，用于预测该类体系的相行为。我们针对每种介观相开发了相应模型，可同时且可靠地模拟两亲分子与脂肪酸的结构、浓度以及温度的影响，并能在未用于模型训练的实验条件下，对这些介观相做出精准预测。