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一个具有参数的PC-SAFT框架,用于在药物聚合物系统中进行溶解度推断

Alex Mathers¹, Michal Fulem¹

¹Department of Physical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic.

Molecular pharmaceutics

|September 26, 2025

概括

这项研究表明,扰乱链统计关联流体理论 (PC-SAFT) 可以通过直接使用实验数据,准确地模拟药物聚合物溶解度. 优化相互作用参数,而不是纯成分值,是可靠的溶解性预测的关键.

科学领域:

热力学是一种热力学.
制药科学制药科学
材料科学材料科学材料科学

背景情况:

准确的药物聚合物溶解度建模对于无形固体分散和先进的制药配方至关重要.
扰乱链统计关联流体理论 (PC-SAFT) 是热力学相互作用的强大框架,但需要验证的参数.
参数可用性和二进制交互优化通常限制PC-SAFT的预测准确性.

研究的目的:

介绍PC-SAFT作为药物聚合物溶解性的推断工具的新型数据驱动应用.
为了使可溶性估计,而不依赖于预设的参数或投机的二进制相互作用调整.
调查纯组件参数值与二进制交互优化对预测性能的影响.

主要方法:

开发了一种数据驱动的PC-SAFT方法,通过将模型参数直接回归到特定药物-聚合物对的实验性可溶性数据.
应用PC-SAFT作为一个推断框架,绕过了文学衍生参数的需求.
进行了单独的分析,以任意纯组件值优化二进制交互参数 (k_ij).

主要成果:

数据驱动的PC-SAFT方法成功地实现了无需预设参数或k_ij调整的可溶性估计.
通过任意纯组件值优化k_ij,预测性能与文献衍生参数相提并论.
这两种策略都可靠地重现了案例研究中的实验溶解度趋势.

结论:

可以有效地使用PC-SAFT作为用于制药溶解度建模的数据驱动推断工具.
二元交互参数发挥着主导作用,这表明详细的纯组件校准可能不必.
这些策略提供了切实可行的方法来弥合药物聚合物热力学建模中的数据缺口.

关键词:

在PC-SAFT中使用.无形固体分散的无形固体分散药物溶解度药物溶解度药物溶解度一个相位图的相位图.聚二氧化) 是一种聚二氧化

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