对两种数据驱动建模方法进行比较研究,以预测ER矩阵片中的药物释放
A S Sousa1, J Serra2, C Estevens2
1Universidade de Coimbra, Faculdade de Farmácia, Coimbra 3000-148 Portugal; Grupo Tecnimede, Quinta da Cerca, Caixaria, Dois Portos 2565-187, Portugal.
International journal of pharmaceutics
|January 18, 2025
概括
这项研究开发了先进的模型来预测延长释放 (ER) 片的药物溶解. 像人工神经网络 (ANN) 和实验功能设计 (FDOE) 这样的数据驱动方法可以准确预测药物释放概况,优化配方开发.
科学领域:
- 制药科学 制药科学
- 药物输送系统 药物输送系统
- 计算化学的计算化学
背景情况:
- 延长释放 (ER) 口服配方提供治疗益处,但在实现持续的药物释放方面面临挑战.
- 在体外溶解测试对于评估ER配方至关重要但耗时.
- 设计质量 (QbD) 原则在制药开发中越来越多地被采用.
研究的目的:
- 开发和比较基于聚乙烯氧化物 (PEO) 的ER口服水友基矩阵片中药物溶解的预测模型.
- 将数据驱动的建模集成到 QbD 框架中,以实现增强的配方开发.
- 提高预测药物释放概况的准确性和效率.
主要方法:
- 模型选和机器学习 (ML) 模型的比较,特别是人工神经网络 (ANN).
- 功能数据分析 (FDA) 结合实验设计 (DoE) 的应用,用于连续溶解曲线建模 (FDOE).
- 使用培训,验证和测试集分析了91种ER矩阵片剂配方的数据集.
主要成果:
- 无论是ANN和FDOE模型都显示出与实验溶解资料的高度相似性 (FDOE的f2值为48-88,ANN的52-88).
- 该研究成功验证了开发的建模方法的预测能力.
- 数据驱动的技术在表征和预测溶解行为方面被证明是有效的.
结论:
- 先进的数据驱动建模技术,包括ANN和FDOE,可以显著提高ER口服配方的溶解预测精度.
- 将这些建模方法集成到基于QbD的开发中,简化了制定过程,减少了开发时间和成本.
- 这项工作支持采用计算工具,以实现更高效的制药产品开发.
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