使用基于SAFT的热力学状态方程预测小分子药品的溶解性参数
Hashem O Alsaab1, Saeed Shirazian2
1Department of Pharmaceutics and Pharmaceutical Technology, Taif University, Taif 21944, Saudi Arabia.
概括
预测药物溶解性参数对于制药配方至关重要. 这项研究引入了扰乱链统计关联流体理论 (PC-SAFT) 状态方程,用于准确预测药物溶解度,其性能优于传统方法.
科学领域:
- 物理化学 物理化学
- 制药科学 制药科学
- 计算化学计算化学
背景情况:
- 准确的药物溶解性参数预测对于药物配方优化至关重要.
- 现有的组贡献 (GC) 方法在捕获固态障碍,分子内键和药品中发现的新化学组方面存在局限性.
- 药物溶解性参数的实验数据往往很少,需要可靠的预测模型.
研究的目的:
- 使用扰乱链统计关联流体理论 (PC-SAFT) 状态方程 (EoS) 估计药物溶解度参数.
- 解决目前用于预测复杂药物化合物的溶解度的组贡献方法的局限性.
- 研究分子间相互作用,特别是键在溶解度参数预测中的作用.
主要方法:
- 使用扰乱链统计关联流体理论 (PC-SAFT) 状态方程 (EoS) 进行溶解度参数估计.
- 从二元实验溶解度数据计算PC-SAFT EoS参数.
- 明确考虑药物-药物和药物-溶剂关联相互作用以提高模型性能.
- 采用无约束回归方法 (URA) 与实验数据来关联汉森溶解度术语作为一种补充方法.
主要成果:
- PC-SAFT方法在预测药物溶解性参数方面表现出令人满意的准确性,优于GC方法.
- 结合相互作用被确定为准确的可溶性参数预测的关键因素.
- PC-SAFT模型成功预测了药品的可溶性参数,包括具有罕见或新功能组的药物.
结论:
- PC-SAFT状态方程为预测药物溶解性参数提供了强大而准确的方法,克服了GC方法的局限性.
- 这种预测工具可以通过优化药物开发和化学过程中的溶剂选择,帮助预先设计新的候选药物.
- 对关联相互作用的明确考虑显著提高了可溶性参数预测的可靠性.
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