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一个多任务建模框架,用于平板电脑的可制造性和质量属性在直接压缩使用知识导向神经网络

Manuel Borja¹, Jens Dhondt², Johny Bertels²

¹Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, Gent, 9000, Belgium.

International journal of pharmaceutics

|October 29, 2025

概括

这项研究引入了一个新的神经网络框架,可以在直接压缩过程中同时预测药物制造能力和质量属性. 这种综合方法通过将原材料特性与最终产品的成功联系起来,加速了配方开发.

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.
计算建模计算建模

背景情况:

药物产品开发需要评估制造可行性和关键质量属性 (CQA).
传统的建模方法经常单独解决可制造性或CQA,忽视它们的内在联系.
直接压缩是固体剂型的常见制造工艺.

研究的目的:

开发一个使用神经网络进行直接压缩的联合建模框架.
同时估计一个配方的可制造性及其质量属性.
加速制药产品的配方和工艺开发.

主要方法:

利用了一个联合的神经网络建模框架.
输入特征包括原材料特性,混合比率和直接压缩加工条件.
通过单调性规则评估交互建模技术,如注意力机制和嵌入专家知识.

主要成果:

证明了用于制造性和质量属性预测的联合建模框架的可行性.
综合方法有效地利用了配方特性,可加工性和最终产品质量之间的固有关系.
注意力机制和单调性规则提高了在捕捉复杂物质相互作用时的模型性能.

结论:

一个联合建模框架提供了一个强大的方法,可以同时预测直接压缩中的可制造性和质量属性.
这种方法可以显著帮助制剂科学家优化药物产品开发.
拟议的框架简化了制定和过程参数的评估,从而加快了开发时间表.

关键词:

直接压缩直接压缩.配方开发配方开发混合型建模混合型建模多任务建模多任务建模神经网络的神经网络的神经网络

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