利用机器学习来预测聚合物药物输送系统中的药物释放.
Sareh Aghajanpour1, Hamid Amiriara2, Mehdi Esfandyari-Manesh3
1Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran.
Computers in biology and medicine
|February 20, 2025
概括
机器学习 (ML) 通过准确预测药物释放配置文件来增强聚合物药物递送系统 (PDDS). 人工神经网络对复杂的系统,如3D打印的剂量表格,特别有希望.
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:
- 聚合物药物递送系统 (PDDS) 对于受控释放至关重要,但面临着配方和预测挑战.
- 传统的方法与PDDS的复杂性和众多影响变量作斗争.
- 机器学习 (ML) 提供了一种有希望的方法来克服药物输送中的这些局限性.
研究的目的:
- 审查ML策略来预测PDDS中的药物释放.
- 突出七个持续释放系统中的关键ML应用.
- 讨论基于ML的药物释放预测中的挑战,解决方案和未来方向.
主要方法:
- 对PDDS应用的基本ML原则的概述.
- 对用于矩阵平板,微球,植入物,水凝,薄膜和3D打印形式的ML的当前研究的分析.
- 评估人工神经网络 (ANN) 和组合模型用于释放预测.
主要成果:
- 人工神经网络在PDDS释放预测方面表现优越,与其他ML方法相比.
- 集成模型对于具有多个参数的复杂释放配置文件是有效的.
- ML显示了3D打印剂型的巨大潜力,使个性化医疗成为可能.
结论:
- ML,特别是ANN,为预测各种PDDS药物释放提供了强大的工具.
- 机器学习促进了复杂药物输送系统的进步,包括3D打印的形式.
- 基于ML的预测为个性化医疗和精确的药物输送铺平了道路.
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