机器学习的拉曼光谱预测药物释放从多糖合物涂层的目标结肠输送药物释放
Youssef Abdalla1, Laura E McCoubrey1, Fabiana Ferraro2
1UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.
概括
机器学习使用拉曼光谱预测结肠向的多糖化物涂层中的药物释放. 这加快了有效的药物输送系统的发展,使得材料的快速选择.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 结肠药物递送提供了诸如直接治疗接入和由于结肠中代谢活性较低而改善的生物可用性等优势.
- 目前选择结肠药物输送材料的方法缓慢,通量低,阻碍了创新.
- 多糖因微生物消化而对结肠向有希望,但它们的异质性使选择复杂化.
研究的目的:
- 开发一种高通量方法,用于选择合适的多糖材料,用于针对结肠的药物输送.
- 利用机器学习和光谱数据来预测多糖涂层中的药物释放.
- 了解多糖的化学特性,影响它们适用于结肠分娩的适用性.
主要方法:
- 利用公开可用的实验数据和机器学习 (ML) 来预测多糖合物涂层中的药物释放 (5-aminosalicylic acid).
- 采用多糖的拉曼光谱作为ML模型输入的特征,一种新的方法.
- 在8个未见的药物释放档案上对新的多糖化物涂层进行了验证的ML模型.
主要成果:
- 在模拟的人类,老鼠和狗结肠环境中使用ML模型成功预测药物释放.
- 通过对新材料的验证,证明了ML方法的通用性和可靠性.
- 确定了多糖的关键化学特征,影响其在结肠药物输送中的表现.
结论:
- 频谱数据与ML相结合,为预测药物配方中的药物释放提供了一个强大的工具.
- 这种方法通过有效和可持续的材料预排名,显著推进了针对结肠的药物输送.
- 为开发更有效,更有针对性的药物输送策略铺平了道路.
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