外消旋曼德酸类化合物的分离机理及手性高效液相色谱谱图:用CREST -XTB进行实验与计算的比较研究

IF 2.5 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Fenti Fatmawati, Aditya Wibawa Sakti, Suci Zulaikha Hildayani,  Akhmaloka, Fida Madayanti Warganegara, Muhamad Abdulkadir Martoprawiro
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引用次数: 0

摘要

背景:市场上近90%的药物是外消旋物。外消旋体是两种对映异构体或物质的混合物,其数量相等,具有不对称的分子结构,互为镜像。手性药物异构体虽然具有相同的化学结构,但在药理学、毒性、药代动力学、代谢等方面表现出截然不同的生物学行为。由于外消旋药物只有一种生物活性对映体,而其对应的对映体具有不良的药理学性质,因此有必要分离这些外消旋化合物以获得所需的活性对映体。色谱法是分离对映体的方法之一。本研究通过手性高效液相色谱柱观察了外消旋扁桃酸化合物的色谱谱。然后通过计算观察色谱剖面来研究分离机理。实验结果与计算分析一致,S层析比r对映体先洗脱。可以预测r -对映体的结合能(-108.92 kJ/mol)比s -对映体的结合能(- 67 kJ/mol)强。方法:采用手性OD柱实验观察外消旋体中杏仁酸的色谱分布,并采用保形-旋体集合采样工具(CREST)计算预测柱-配体结合能。采用0.46 cm × 25 cm手性OD柱高效液相色谱仪(Daicel Chemical)进行图谱鉴定。所用样品为扁桃酸和标准(S)-扁桃酸的外消旋化合物。采用Windows 11 Pro 64位操作系统,基于x64的处理器,配备MGL-Tools程序,该程序包括ADT (Autodock Tools)应用程序、Avogadro、Autodock 4.2、Discovery Studio 2020 Client®和CREST,并安装为XTB半经验量子化学包的驱动程序,计算每个对映体的柱容量因子和结合能。对于dmpc配体配合物的几何优化和采样,我们使用了CREST,网址为https://github.com/grimme-lab/crest。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Separation mechanism and chiral-HPLC chromatogram profile of racemic mandelate compounds: a comparative study between experiment and computation using conformer–rotamer ensemble sampling tool (CREST)-XTB

Separation mechanism and chiral-HPLC chromatogram profile of racemic mandelate compounds: a comparative study between experiment and computation using conformer–rotamer ensemble sampling tool (CREST)-XTB

Context

Nearly 90% of drugs on the market are racemates. A racemate is a mixture of two enantiomers or substances in equal amounts that have an asymmetric molecular structure that is a mirror image of each other. Despite having the same chemical structure, chiral drug isomers can exhibit very different biological behaviors in terms of pharmacology, toxicity, pharmacokinetics, metabolism, etc. Since racemic drugs have only one bioactive enantiomer while its counterpart enantiomers impart undesirable pharmacological properties, it is necessary to separate these racemic compounds to obtain the desired active enantiomer. Chromatography is one of the approaches for the separation of enantiomers. In this study, we observed the chromatographic profile of racemic mandelic acid compound passed through a chiral HPLC column. The chromatogram profile was then observed computationally to study the separation mechanism. The experimental results are in line with the computational analysis that the S chromatogram eluted first compared to the R-enantiomer. It can be predicted that the binding energy of the R-enantiomer (–108.92 kJ/mol) is stronger than the S-enantiomer (− 67 kJ/mol).

Methods

The chromatogram profile of mandelic acid racemate was observed experimentally using a chiral OD column, and the prediction of column-ligand binding energy was based on computational studies using the conformer–rotamer ensemble sampling tool (CREST). The chromatogram profile was identified using a 0.46 cm × 25 cm chiral OD column HPLC instrument (Daicel Chemical). The samples used were racemic compounds of mandelic acid and standard (S)-mandelic acid. Computational calculations of column capacity factors and binding energies of each enantiomer were performed with a Windows 11 Pro 64-bit operating system, × 64-based processor, equipped with the MGL-Tools program consisting of the ADT (Autodock Tools) application, Avogadro, AutoDock 4.2, Discovery Studio 2020 Client®, and CREST installed as a driver program for the XTB semiempirical quantum chemistry package. For geometry optimization and sampling of DMPC-ligand complexes, we used CREST at https://github.com/grimme-lab/crest.

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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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