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通过机器学习框架来缓解分裂,提高了滚筒压缩过程中的带质量
Mohammad Shahab1, David Sixon1, Jayden A Pierce2
1Davidson School of Chemical Engineering, Purdue University, 47907, IN, USA.
International journal of pharmaceutics
|September 21, 2025
概括
这项研究引入了一种机器学习框架,用于预测和理解干燥颗粒处理中的带分裂. 该模型准确地预测了带质量,确保了稳定的平板电脑生产和监管合规性.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 工艺工程是过程工程.
- 数据科学在制药领域
背景情况:
- 在滚筒压缩过程中,带分裂会损害颗粒的均性和平板质量.
- 由于复杂的过程变量相互作用,预测带分裂是具有挑战性的.
- 目前的方法缺乏准确性,无法完全理解和控制这种现象.
研究的目的:
- 开发一种机器学习框架,用于模拟和表征干颗粒化中带分裂.
- 改进带质量的预测,并确定最佳的工艺条件.
- 通过特征重要性分析,提高带分裂现象的解释性.
主要方法:
- 使用基于高斯过程回归 (GPR) 的神经网络与转移学习.
- 为了模型的解释性和特征的重要性,采用了夏普利添加式解释 (SHAP).
- 利用多变量实验数据进行模型培训和验证.
主要成果:
- 实现了带质量 (厚度,密度) 的可靠预测,具有高R2和低MSE/MAE.
- 确定了可行的操作区域和最佳条件,以保持一致的产品质量.
- 证明了框架的灵活性,可扩展性和跨条件的通用性.
结论:
- 开发的机器学习框架有效地模拟和预测了带分裂.
- 该方法促进了早期故障检测,加速了流程开发,并支持QbD.
- 能够为制药干颗粒的闭环控制提供可操作的见解.
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