机器学习根据药片配方预测药物释放概况和动力参数
Chrystalla Protopapa1, Angeliki Siamidi1, Amelia Adibe Eneli2
1Section of Pharmaceutical Technology, Department of Pharmacy, National and Kapodistrian University of Athens, 157 84, Athens, Greece.
机器学习 (ML) 模型可以从直接压缩 (DC) 配方中预测药物释放概况,加速药物开发. 这种方法提供了对运动参数的洞察,改善了固体剂型的配方优化.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 直接压缩 (DC) 是用于制造固体剂型的广泛使用的方法.
- 对直流的配方优化传统上是耗时和资源密集的.
研究的目的:
- 评估机器学习 (ML) 在动态条件下预测药物释放配置文件的实用性.
- 加速直接压缩配方的开发和优化.
主要方法:
- 生产了377种直接压缩配方,并测量了它们的动态溶解配置.
- 使用六种机器学习技术来预测释放配置文件和运动参数.
- 随机森林 (RF) 和极端梯度增强 (XGB) 模型使用R平方值进行了评估.
主要成果:
- ML模型,特别是RF和XGB,在预测整个药物释放概况方面表现出能力.
- 实现了0.635 ± 0.047 (RF) 和0.601 ± 0.091 (XGB) 的五倍交叉验证R平方.
- 预测动力参数的二次策略产生了可比的结果,提高了模型的解释性.
结论:
- 机器学习可以显著加快在动态溶解研究期间药物释放的预测.
- 机器学习模型为运动参数提供了有价值的见解,有助于制药研究人员在配方开发中.
- 未来的研究将专注于开发更多"动力信息"的ML模型,以提高预测能力.
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