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新型聚合物辅助剂的数据驱动设计用于制药无形固体分散的新型聚合物辅助剂

Elena J Di Mare¹, Ashish Punia², Matthew S Lamm²

¹Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.

Bioconjugate chemistry

|August 16, 2024

概括

研究人员开发了一种机器学习模型,用于预测无形固体分散 (ASD) 中的高玻璃过渡温度 (Tg). 这有助于设计稳定的口服药物输送系统,用于溶性较差的活性药物成分 (API).

科学领域:

制药科学制药科学
聚合物化学聚合物化学
材料科学材料科学材料科学

背景情况:

许多活性药物成分 (API) 具有较差的水溶性和较低的口服生物可用性,阻碍了药物输送.
无形固体分散 (ASD) 是通过防止聚合物矩阵内的API结晶来增强溶解性和生物可用性的关键策略.
了解ASD中的聚合物的结构功能关系对于开发有效配方至关重要,高玻璃过渡温度 (Tg) 是关键性能指标.

研究的目的:

研究聚合物结构特征如何影响无形固体分散 (ASD) 的玻璃过渡温度 (Tg).
设计具有改进Tg的新型共聚物,以提高ASD稳定性和性能.
开发一个预测机器学习 (ML) 模型,用于识别高Tg ASD 配方.

主要方法:

通过自动光诱导的电子/能量转移-可逆添加-碎片化链转移 (PET-RAFT) 聚合,合成一套共聚合物的库.
使用模型药物probucol.的50种无形固体分散剂 (ASD) 的制备和表征.
应用机器学习 (ML) 算法,特别是随机森林回归器,在Tg数据上训练,以确定关键结构-Tg关系.

主要成果:

该ML模型准确地预测了聚合物和带有probucol的ASD的Tg,在10倍的交叉验证中实现了平均R2>0.83.
确定了影响Tg的关键聚合物特征,包括骨干甲基化和非线性侧链几何.
该研究表明了ML在指导具有理想固态特性的ASD设计方面的潜力.

结论:

机器学习有效地捕捉了ASD中的结构-Tg关系,从而能够预测新型共聚合物.
开发的ML模型作为一个强大的工具来设计高Tg的ASD,这对于提高货架稳定性和防止药物结晶至关重要.
这种方法促进了先进的口服药物输送系统的合理设计,用于难以溶解的API.

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