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高斯过程建模用于预测聚合物纳米粒子配方设计,以提高封装效率和治疗疗效

Sihan Dong¹, Haolin Yu², Pascal Poupart²

¹School of Pharmacy, University of Waterloo, Ontario, N2G 1C5, Canada.

Drug delivery and translational research

|May 20, 2024

概括

机器学习,特别是高斯过程模型,可以加速纳米粒子 (NP) 药物递送系统的优化. 这些模型准确地预测了药物封装效率和治疗疗效对于多 (乳糖-同-甘油酸) (PLGA) NPs.

科学领域:

生物材料科学生物材料科学
纳米技术纳米技术
计算化学的计算化学

背景情况:

传统的药物输送面临着诸如第一通代谢和非目标效应等挑战.
纳米粒子 (NP) 通过改善药物输送和减少副作用提供了一个解决方案.
优化NP材料成分至关重要,但往往耗时且昂贵.

研究的目的:

探索机器学习 (ML) 的使用,以加速纳米粒子 (NP) 组成优化.
开发高斯过程 (GP) 模型来预测药物封装效率 (EE%) 和NP的治疗疗效.
为了验证GP模型的预测准确性,使用聚 (乳酸-co-糖醇酸) (PLGA) 纳米粒子配方.

主要方法:

制造了32种具有不同物理化学性质的聚 (乳糖合甘油酸) (PLGA) 纳米粒子配方.
装载着模型药物的NP:多克索鲁比辛 (DOX) 和多塞塔克塞尔 (DTX).
利用高斯过程 (GP) 模型预测EE%和IC50值 (治疗疗效) 对OVCAR3卵巢癌细胞.

主要成果:

EE% GP模型实现了最高的预测准确性,正常化的RMSE为0.187.
药物疗效的GP模型显示DOX的0.296和DTX的0.206的正常化RMSE.
这些结果表明了GP模型在预测NP性能方面的潜力.

结论:

高斯过程模型可以显著加速纳米粒子药物输送系统的优化.
基于ML的EE%和治疗疗效的预测是可行的和准确的.
这种方法可以减少与开发新型NP药物输送配方相关的时间和成本.

关键词:

配方设计配方设计斯过程是高斯过程.机器学习是机器学习.聚合物纳米粒子聚合物纳米粒子

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