通过SDS和机器学习,提炼高药载荷膏的风质特性
Xilong Qian1,2,3, Kewei Wang4, Yulu Ma1,3
1State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Nanjing University of Chinese Medicine, Nanjing, China.
PloS one
|May 9, 2024
概括
这项研究优化了使用确定查设计 (DSD) 进行传统中医药 (TCM) 药膏制备. 研究确定了提高TCM药膏制造效率和质量的最佳参数.
科学领域:
- 制药科学 制药科学
- 传统中国医学配方 配方
- 过程优化 过程优化
背景情况:
- 教授WYH的传统中医药 (TCM) 药膏具有临床证明的疗效.
- 标准化的制备过程对于确保TCM配方的一致的质量和治疗效果至关重要.
- 优化外部药膏制备需要系统地调查关键过程参数.
研究的目的:
- 为了优化WYH教授TCM外用药膏的准备过程.
- 通过使用先进的统计方法,识别影响膏药质量的关键过程参数.
- 为TCM药膏生产提供可复制和科学验证的方法.
主要方法:
- 使用最终选设计 (DSD) 进行高效的过程因素选.
- 逐步回归分析模拟了过程变量与质性质之间的关系.
- 机器学习技术,包括蒙特卡洛模拟,决策树分析和高斯过程,用于参数优化.
主要成果:
- 成功确定和验证了WYH药膏的最佳制备过程.
- 主要优化参数包括药物比率 (24.5%),混合时间 (8分钟),混合速度 (1175rpm),石油剂量 (79g) 和液态剂量 (6.7g).
- 方法B被确定为最终配方的最佳制备技术.
结论:
- 优化的制备过程提高了TCM药膏制造的科学严谨性和质量.
- 该研究为开发其他TCM药膏提供了有价值的见解和实际指导.
- 应用DSD和机器学习为优化复杂药物配方提供了强大的方法.
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