在压缩型号上使用LSTM网络预测平板电脑的抗拉强度
Jill De Bisshop1, Stefan Klinken2
1Department of Pharmaceutics, Ghent University, Ghent, Belgium.
International journal of pharmaceutics
|July 30, 2023
概括
这项研究预测了使用具有长短期记忆 (LSTM) 层的反复神经网络 (RNN) 的平板电脑的抗拉强度. 该模型从工艺数据准确地预测了表格性,有助于配方开发和生产.
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:
- 平板电脑的抗拉强度是制药制造中的关键质量属性.
- 在开发早期预测诸如抗拉强度之类的机械性能,可以优化配方和工艺设计.
- 评估表化性的传统方法可能是耗时且资源密集的.
研究的目的:
- 开发和验证平板电脑抗拉强度的预测模型.
- 为了利用过程数据,包括压缩压力,时间和冲孔位置,用于拉伸强度预测.
- 评估模型在各种材料和压缩条件下的性能.
主要方法:
- 使用了具有长短期记忆 (LSTM) 层的循环神经网络 (RNN) 架构.
- 该模型在一个包含344个平板电脑压缩周期的数据集上进行了训练.
- 模型的性能在一组独立的测试中评估了804个药片周期.
主要成果:
- 开发的LSTM模型实现了高预测准确度,测试数据的确定系数 (R2) 为0.954.
- 预测错误与材料类型,混合物组成,压缩压力或片剂质量没有显著的相关性.
- 该模型允许从12种物质的单次压缩事件中确定完整的可表化概况.
结论:
- 循环神经网络,特别是LSTM,对于从工艺数据中预测平板电脑的抗拉强度是有效的.
- 这种数据驱动的方法为加速配方研发和增强生产过程控制提供了一个有前途的工具.
- 该模型能够有效地预测可表化概况的能力可以简化药物开发工作流程.
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