建模聚合物药物释放:机器学习的新兴作用
Ryan N Woodring1, Kristy M Ainslie1,2,3
1Division of Pharmacoengineering & Molecular Pharmaceutics, Eshelman School of Pharmacy, UNC, Chapel Hill, North Carolina, USA.
概括
机器学习 (ML) 通过预测聚合物药物释放动力学来推进药物输送,克服传统模型的局限性. 这使得基于聚合物的新药配方能够更快地开发.
科学领域:
- 聚合物科学 聚合物科学
- 药物输送系统是药物输送系统.
- 计算建模计算建模
背景情况:
- 聚合物药物释放的传统机理学和实证模型依赖运输现象,但经常使用简化假设.
- 这些模型通常仅限于对体外数据的回顾性分析,这对准确的预测性特征提出了挑战.
- 从聚合物配方中准确描述时间依赖的药物释放仍然是制药开发中的重大挑战.
研究的目的:
- 探索从传统数学模型向机器学习 (ML) 方法的过渡,以描述和预测聚合物系统中的药物释放.
- 为在配方开发中应用ML提供实用路线图,将AI与既有知识整合在一起.
- 突出ML在加速设计和翻译先进的基于聚合物的药物输送系统方面的潜力.
主要方法:
- 在药物释放模型中使用的传统数学和物理原理 (扩散,胀,侵蚀) 的审查.
- 探索人工智能 (AI) 的最新进展和应用,特别是机器学习 (ML),用于药物释放的表征和预测.
- 讨论机器学习应用中的关键趋势,包括数据编译,处理,架构选择和性能指标.
主要成果:
- 机器学习模型为从聚合物系统中表征和预测药物释放动力学提供了一个新的前沿.
- 机器学习可以揭示关键的配方参数,这些参数控制着独特的药物释放概况,支持高效的开发.
- 最近的机器学习应用表明,在提高超越传统建模方法的预测能力方面,有很大的潜力.
结论:
- 机器学习提供了强大的工具,可以克服传统模型在描述时间依赖药物释放方面的局限性.
- 将ML与现有知识相结合,可以加速下一代基于聚合物的药物递送系统的开发和翻译.
- 本综述为科学家提供了一份路线图,以利用ML为先进的配方开发和创新.
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