机械,数据驱动和混合模型:替代药物溶解建模中的关键比较
Barbara Honti1, Gréta Mihályi1, Zsombor Kristóf Nagy1
1Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
International journal of pharmaceutics
|December 7, 2025
概括
数学建模有助于制药开发. 结合人工神经网络 (ANN) 和人口平衡模型 (PBM) 的新型混合模型准确预测药物溶解,优于独立模型.
科学领域:
- 制药科学 制药科学
- 化学工程是化学工程的重要组成部分.
- 计算机建模 计算建模
背景情况:
- 数学建模对于制药设计质量 (QbD) 是至关重要的.
- 它有助于理解过程并预测药物产品的性能.
- 评估药物溶解的不同建模方法是必不可少的.
研究的目的:
- 批判性地比较基于知识的,数据驱动的,以及用于体外溶解的混合建模.
- 引入一种结合人工神经网络 (ANN) 和人口平衡模型 (PBM) 的新型混合模型.
- 评估模型性能使用实验数据对乙盐酸 (ASA) 片.
主要方法:
- 开发并比较了ANN,PBM和一个新的ANN-PBM混合模型.
- 利用在不同条件下制造的乙盐酸 (ASA) 片中的体外溶解数据 (实验设计).
- 在ANN模型中研究了不同的粒子大小分布 (PSD) 表示.
主要成果:
- 使用平均粒子大小指标的ANN模型实现了最佳预测准确度 (2.14%的训练,5.49%的验证RMSE).
- 新的混合ANN-PBM模型表现出优于独立PBM的性能 (6.22%的培训,8.90%的验证RMSE).
- 所有评估的模型都适合立即释放预测;ANN和混合模型有效地描述了缓慢溶解.
结论:
- 数据驱动的ANN模型为药物溶解提供了出色的预测能力.
- 混合建模为预测溶解评估提供了可靠和可解释的替代方案.
- 新的混合模型增强了过程理解,并支持QbD在制药中的实施.
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