通过BBD-ANN-AGWO框架优化多目标喷雾干燥工艺:安德罗格拉福利德无形固体分散的案例
Guangpu Fang1,2, Changhao Jia1,2, Zhiqi Guan1
1College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
AAPS PharmSciTech
|February 25, 2026
概括
本研究介绍了一种智能框架,用于优化喷雾干燥过程,使用人工神经网络和自适应灰狼优化器. 该方法通过提高能源效率,产量和药物释放来增强药物配方开发.
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.
- 计算化学计算化学
背景情况:
- 喷雾干燥是制药配方中的一个关键过程,用于创建无形固体分散.
- 优化多目标喷雾干燥参数 (例如能源消耗,产量,药物释放) 是复杂的.
- 安德罗格拉福利德 (ADG) 无形固体分散作为过程优化的模型系统.
研究的目的:
- 建立一个多目标喷雾干燥过程优化框架.
- 整合盒式设计 (BBD),人工神经网络 (ANN) 和自适应灰狼优化器 (AGWO).
- 为了优化andrographolide (ADG) 无形固体分散的配方.
主要方法:
- 分子对接选了潜在的聚合物载体,以获得最佳的溶解性能.
- 在体外溶解实验中确定了表现最好的载体.
- 单因素和BBD试验评估了过程参数对关键结果的影响.
- 开发了一个多响应的ANN模型,并增加了虚拟样本.
- AGWO进行了反向优化,以确定最佳的过程参数.
主要成果:
- 实验验证证证实了该模型的预测准确性.
- 通过X射线衍射 (XRD) 和里埃变换红外 (FT-IR) 分析,验证了ADG的无形变换和稳定性.
- 优化过程显示了令人满意的短期物理稳定性.
- 该框架成功地平衡了多个优化目标.
结论:
- 拟议的框架为制药配方开发提供了可行和智能的战略.
- 整合BBD,ANN和AGWO可以实现高效的多目标优化.
- 这种方法可用于优化各种制药过程,以提高药物输送效率.
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