最近使用环保超临界CO2提高药物溶解性的进展:机器学习视角
1Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Frontiers in medicine
|September 17, 2024
概括
机器学习 (ML) 模型使用超临界二氧化碳 (SCCO2) 预测药物溶解度和生物可用性,为有机溶剂提供环保的替代品. 这种方法提高了药物制造和治疗疗效.
科学领域:
- 制药科学 制药科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 药物溶解率和溶解率较低显著限制了治疗疗效,这在药物开发中构成了重大挑战.
- 超临界流体 (SCF),特别是超临界二氧化碳 (SCCO2),正在成为药物加工中危险有机溶剂的环保和高效替代品.
- 药物溶解性和生物可用性的实验性确定通常是耗时且昂贵的.
研究的目的:
- 审查机器学习 (ML) 工具的应用,以使用SCCO2.2预测药物溶解性和生物可用性.
- 讨论药物溶解度在制药行业中的重要性,以及改善药物溶解度较差药物的方法.
- 探索SCCO2在改善药物纳米晶体制造过程中的实用性.
主要方法:
- 用于药物溶解性和生物可用性预测的ML模型的文献综述.
- 对SCCO2特性及其与传统溶剂相比的优势的分析.
- 讨论改善可溶性和纳米晶体形成的技术.
主要成果:
- ML模型在预测药物溶解性和生物可用性参数方面表现出高准确度.
- SCCO2为药物加工提供了安全,环保和有效的介质,提高了溶解度,并使纳米晶体生产成为可能.
- 有各种策略可以提高难溶性药物的可溶性.
结论:
- 基于ML的预测工具与SCCO2技术相结合,提供了一种强大而可持续的方法来克服药物溶解性挑战.
- SCCO2促进了高效的药物制造,特别是纳米晶体配方,从而改善了治疗结果.
- 这种综合方法对推进制药开发和药物输送系统具有重大前景.
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