针对药物输送的脂质体配方在机器学习驱动的进步:叙事文献综述
Benyamin Hoseini1, Mahmoud Reza Jaafari2,3, Amin Golabpour4
1Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Current drug delivery
|June 28, 2024
概括
机器学习 (ML) 通过优化粒子大小和药物负载等关键参数来增强纳米脂体配方开发. 本综述探讨了ML的整合,以改善有针对性的药物输送系统.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 生物技术是生物技术.
背景情况:
- 纳米脂体配方是先进的药物递送系统,使用脂质双层进行向治疗剂封装.
- 机器学习 (ML) 越来越多地被探索,以优化脂肪体配方所涉及的复杂过程.
- 了解ML的作用对于提高向药物递送效率至关重要.
研究的目的:
- 审查将ML集成到纳米脂质体配方开发中的动机和应用.
- 提供对ML在这个领域的优势的细微了解.
- 提出一个概念模型,以便在脂质体研究中有效地纳入ML.
主要方法:
- 系统审查目前关于ML在脂质体配方中的应用的研究.
- 讨论ML原则,技术 (整体学习,决策树,神经网络),以及与脂质体相关的特征选择.
- 在ML驱动的配方研究中分析常见的评估指标和验证策略.
主要成果:
- ML技术有效地优化了关键的配方参数:封装效率,颗粒大小,药物负载和多分散度指数.
- 对于需要标记数据的结构化表述,监督学习模型被突出显示.
- 平均绝对误差是一个关键指标,强调预测和实际值之间的一致性.
结论:
- ML 作为一个决策支持系统,用于优化纳米脂质体配方.
- 为未来的研究提出了一个结构化的框架,包括实验,分析和代的ML改进.
- 倡导无ML集成,强调协作和验证,以实现脂质体药物递送的强有力的进步.
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