开发一种HPLC-UV方法,用于在小体积的血样本中量化波萨可纳:实验设计和机器学习模型的设计
Fereshteh Bayat1, Ali Hashemi Baghi2, Zahra Abbasian1
1Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, PO Box: 14155‒6153, Tehran, Iran.
BMC chemistry
|December 5, 2024
概括
这项研究使用实验设计和机器学习开发了血中波萨康纳 (PCZ) 的快速分析方法. 最优化的方法实现了准确的药理动力学分析的低量化极限 (LOQ).
科学领域:
- 分析化学 分析化学
- 药理动力学 药理动力学
- 计算化学计算化学
背景情况:
- 波萨可纳 (PCZ) 是一种广泛的三醇抗真菌剂.
- 在生物样本中精确量化PCZ对于治疗药物监测和药理动力学研究至关重要.
- 现有的方法可能缺乏对小体积样本所需的灵敏度或速度.
研究的目的:
- 开发和验证一种快速,灵敏的分析方法,用于在小体积的血样本中量化波萨可纳.
- 优化色谱和提取条件使用分数因数设计和机器学习的组合.
- 应用验证的方法对PCZ纳米细胞在老鼠中的药理动力学分析.
主要方法:
- 采用了2级分数因数设计来优化染色学和固相提取 (SPE) 参数.
- 机器学习模型被用来预测和改进最佳实验条件.
- 方法验证是根据国际协调委员会 (ICH) 的指导方针进行的,包括线性和量化极限 (LOQ) 确定.
- 具有紫外线检测的高性能液态染色学 (HPLC) 用于分离和量化.
主要成果:
- 优化的染色学条件产生了8.2±0.2分钟的保留时间.
- 在优化的提取条件下,PCZ从500μL血中恢复超过98%的PCZ.
- 经过验证的方法证明了在50-2000 ng/mL范围内的线性,LOQ为50 ng/mL.
- 成功确定了大鼠的药理动力学参数,包括半衰期 (t1/2),平均停留时间 (MRT) 和AUC.
结论:
- 在低体积血样本中成功开发和验证了一种新的,快速和敏感的分析方法.
- 实验设计和机器学习的整合显著改善了优化过程.
- 经过验证的方法适用于分析纳米细胞配方中的波萨科纳和进行药理动力学研究.
相关概念视频
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