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Updated: Jun 17, 2025

A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products

A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products

Published on: October 4, 2019

以效果为导向的平面色谱的多目标优化作为一种有前途的工具,用于快速选择多能天然产品

Svetlana Ćujić¹, Milica Jankov², Petar Ristivojević³

¹University of Belgrade - Faculty of Chemistry, Studentski trg 12-16, Belgrade 1100, Serbia; Patent Co Ltd., Vlade Ćetkovića 1A, Mišićevo 24211, Serbia.

Journal of chromatography. A

|August 14, 2024

概括

这项研究引入了一种新的方法,用于选择使用效果导向HPTLC和多目标优化的多能天然产品. 德林格 (Derringer) 是一个叫做德林格 (Derringer) 的产品.

科学领域:

自然产品化学自然产品化学
分析化学分析化学
化学信息学化学信息学

背景情况:

选择多能天然产品对于药物发现至关重要.
高性能薄层染色学 (HPTLC) 与生物试验相结合,是这种选择的强大工具.
多目标优化算法可以帮助分析复杂的HPTLC数据.

研究的目的:

开发和评估一种新的方法,以有效地选择多能天然产品.
为了比较不同多目标优化算法的性能,分析HPTLC生物试验数据.
建立基于色谱特征的强效天然产品识别的预测模型.

主要方法:

以效果为导向的HPTLC与生物测试相结合.
应用三种多目标优化算法:德林格的可取性,通过与理想解决方案相似的优先顺序技术 (TOPSIS) 和排名差异总和 (SRD).
刀交叉验证和部分最小方程 (PLS) 回归用于模型开发和验证.

主要成果:

德林格的可取性和TOPSIS在基于多能性的排名提取物方面显示出高度相似性.
与德林格和TOPSIS相比,SRD的排名结果略有不同.
基于德林格的可取性 (R2 = 0.885) 和TOPSIS (R2 = 0.986) 的PLS模型在预测提取物可取性方面表现出高的统计性能.
对SRD值的PLS建模没有成功.

结论:

使用以效果为导向的HPTLC和多目标优化 (德林格的可取性和TOPSIS) 的拟议方法对于选择多能天然产品是有效的.
PLS回归可以成功地应用于HPTLC化学指纹,用于预测新提取物的可取性.
这种方法提高了天然产品发现的效率和准确性.

关键词:

以结果为导向的分析.高性能薄层染色学高性能薄层染色学线性建模线性建模多目标优化多目标优化多能天然产品多能天然产品.

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