通过回顾性机器学习方法论进行合理的配方设计:案例研究 布洛芬
Dylan Garamani1, Erik Sjögren1, Albert Mihranyan1
1Department of Pharmaceutical Biosciences, Uppsala University, Sweden.
International journal of pharmaceutics
|February 13, 2026
概括
机器学习确定了立即释放型布洛芬配方中的关键模式. 特定的布洛芬变体显著改善药物释放,减少药物动力学变异性.
科学领域:
- 制药科学 制药科学
- 药理动力学 药理动力学
- 计算化学计算化学
背景情况:
- 合理的药物配方对于可预测的治疗结果至关重要.
- 了解辅助剂和活性成分对药理动学的影响,对于立即释放的口服剂型至关重要.
研究的目的:
- 通过使用机器学习 (ML) 调查合理的配方设计,以立即释放布洛芬口服剂量产品.
- 为了确定影响布洛芬配方药理动力学特征的模式.
主要方法:
- 使用Python中的pandas提取和标准化注册表数据.
- 分析了溶解修饰辅助剂和ibuprofen变体的分析模式.
- 研究了这些模式对临床药理动力学概况的影响.
主要成果:
- 常见的是含有布洛芬酸和 Lauryl 硫酸盐的薄膜片.
- 布洛芬的变种 (二水合,氨酸,氨酸) 释放速度更快,Tmax降低,Cmax增加,生物可用性变异较低.
- 确定了影响布洛芬药理动学的关键配方模式.
结论:
- 机器学习有助于理解ibuprofen的合理配方策略.
- 特定的布洛芬变体提供了改善的药理动力学特征.
- 研究结果支持对可预测的生物可用性和可复制的临床反应的监管决策.
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