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设计和优化使用机器学习和分子动力学模拟的Indomethacin纳米晶体

Jianlu Qu¹, Chaoliang Jia¹, Yaobin Chen¹

¹College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.

AAPS PharmSciTech

|March 4, 2026

概括

这项研究使用机器学习和汉默声学共振 (HAR) 技术开发了一种稳定的印美他辛 (IND) 纳米晶药物递送系统,提高了溶解性,并证明了难以溶解药物的可扩展性.

科学领域:

制药科学制药科学
材料科学材料科学材料科学
计算化学计算化学

背景情况:

开发有效的药物输送系统,用于水溶性较差的药物,如印梅他辛 (IND),对于提高治疗效果至关重要.
纳米晶体配方的传统方法可能耗时且具有挑战性.
集成先进技术是有效和可扩展的药物输送系统的发展所需的.

研究的目的:

使用机器学习和汉默声学共振 (HAR) 技术开发一种稳定的纳米晶体药物递送系统,用于印梅他辛 (IND).
提高IND的可溶性和溶解率.
研究开发的纳米晶体系统的可扩展性和固态特性.

主要方法:

使用HAR技术进行高通量选,以确定最佳稳定剂 (P188-PVA).
分子动力学模拟以了解药物稳定剂相互作用.
盒子-Behnken设计 (BBD) 和人工神经网络 (ANN) 用于系统优化配方和HAR过程参数.
使用HAR技术进行扩大规模的研究 (5倍和50倍).
干燥技术的评估 (冷干燥,喷雾干燥,液化床干燥).
使用PXRD,DSC和体外溶解研究进行表征.

主要成果:

确定P188-PVA作为IND纳米晶悬浮物的最佳稳定剂.
使用集成的BBD-ANN建模优化IND纳米晶体配方和HAR工艺条件.
通过使用HAR技术,成功扩大IND纳米晶体配方的5倍和50倍.
证实了IND纳米晶体的晶体性质,以及与原始IND相比,它们在体外溶解的显著改善.
证明了该方法对其他水溶性较差的药物的潜在适用性.

结论:

通过将机器学习与HAR技术相结合,迅速开发出一种稳定且可扩展的纳米晶体药物输送系统.
开发的系统显著提高了IND的溶解性和溶解性,显示了改善难溶性药物的生物可用性的前景.
拟议的方法提供了一种多功能方法,用于有效开发和扩大各种具有挑战性的药物化合物的纳米晶体配方.

关键词:

人工神经网络的人工神经网络盒子-Behnken设计的设计.印度甲素 (Indomethacin) 是一种分子动力学模拟模拟一个纳米晶体.

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