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在线NIR与机器学习相结合,可以预测PLA-阿司匹林的机械特性和溶解概况

Nimra Munir^1,2, Tielidy de Lima³, Michael Nugent³

¹Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, Co. Sligo F91 YW50 Ireland.

Functional composite materials

|October 11, 2024

概括

本研究引入了一种实时方法,使用在线传感器和机器学习来预测在热挤出 (HME) 过程中载有药物的聚合物设备的关键质量属性. 这种方法为传统的离线测试提供了更快,非破坏性的替代方案,以改善流程监控.

科学领域:

制药技术制药技术制药技术
过程分析技术 (PAT) 是一种分析技术.
材料科学材料科学材料科学

背景情况:

药物装载的聚合物设备的传统离线测试具有破坏性,缓慢且昂贵.
关键质量属性 (CQA),如溶解概况和机械性能,对于质量保证至关重要.
需要实时的,非破坏性的方法来在生产过程中监测这些CQA.

研究的目的:

开发和验证一种实时方法,用于预测聚合物药物输送装置的机械性能和药物溶解概况.
为此目的,探索在线近红外 (NIR) 和温度传感器与机器学习 (ML) 算法相结合的使用.
为了证明在热挤出 (HME) 过程中预测CQAs的可行性.

主要方法:

在热挤出 (HME) 过程中使用了直线NIR和温度传感器.
采用了各种机器学习 (ML) 算法,包括随机森林 (RF),K-最近邻居 (KNN) 和用RF (RFE-RF) 消除递归特征.
相关的传感器数据与离线测量拉伸强度和药物溶解在6小时和96小时.

主要成果:

非线性ML方法在预测CQAs方面显著超过线性方法.
在拉伸强度预测方面,RFE-RF和KNN分别实现了98%和99%的R2值.
对于预测药物在96小时后释放的R2值,KNN和RFE-RF的R2值分别为97%和96%.

结论:

这项研究成功地证明了在HME期间从线内数据中预测机械性质和药物溶解.
开发的实时方法为制药制造业的传统离线测试提供了一个有希望的替代方案.
这种方法可以提高药物装载聚合物设备的过程监控和优化.

关键词:

溶解特征溶解特征药物输送系统是药物输送系统.机器学习机器学习机械性质机械性质这就是PATAT PATAT.聚合物药物输送方式实时监控实时监控

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