Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Rabia Riaz, Shagufta Parveen, Nusrat Shafiq, Awais Ali, Maryam Rashid
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Abstract

Fagonia indica from Zygophyllaceae family is a medicinal specie with significant antidiabetic potential. The present study aimed to investigate the in vitro antidiabetic activity of Fagonia indica crude extract followed by an in silico screening of its phytoconstituents. For this purpose, crude extract of Fagonia indica was prepared and divided in three different parts, i.e., n-hexane, ethyl acetate, and methanolic fraction. Based on in vitro outcomes, the phytochemical substances of Fagonia indica were virtually screened through a literature survey and a screening library of compounds (1-13) was prepared. The clinical potential of these novel drug candidates was assessed by applying an ADME screening profile. Findings of SwissADME indicators (Absorption, Distribution, Metabolism, and Excretion) for the compounds (1-13) presented relatively optimal physicochemical characteristics, drug-likeness, and medicinal chemistry. The antidiabetic action of these leading drug candidates was optimized through molecular docking analysis against 3 different human pancreatic α-amylase macromolecular targets with (PDB ID 1B2Y), (PDB ID 3BAJ), and (PDB ID: 3OLI) by applying Virtual Docker (Molegro MVD). Metformin was taken as a reference standard for the sake of comparison. In vitro antidiabetic evaluation gave good results with promising α-amylase inhibitory action in the form of IC50 values, as for n-hexane extract = 206.3 µM, ethyl acetate = 41.64 µM, and methanolic extract = 9.61 µM. According to in silico outcomes, all 13 phytoconstituents possess the best binding affinity with successful MolDock scores ranging from - 97.2003 to - 65.6877 kcal/mol and show a great number of binding interactions than native drug metformin. Therefore, the current work concluded that the diabetic inhibition prospective of extract and the compounds of Fagonia indica may contribute to being investigated as a new class of antidiabetic drug or drug-like candidate for further studies.

Abstract Image

针对抗糖尿病靶点的 Fagonia indica 化学成分的虚拟筛选、ADME 预测、药物相似性和分子对接分析。
Fagonia indica 是一种具有显著抗糖尿病潜力的药用植物。本研究旨在研究 Fagonia indica 粗提取物的体外抗糖尿病活性,然后对其植物成分进行硅学筛选。为此,研究人员制备了 Fagonia indica 的粗提取物,并将其分为三个不同的部分,即正己烷、乙酸乙酯和甲醇部分。根据体外结果,通过文献调查对 Fagonia indica 的植物化学物质进行了实际筛选,并编制了化合物筛选库(1-13)。这些候选新药的临床潜力通过应用 ADME 筛选概况进行了评估。化合物(1-13)的瑞士 ADME 指标(吸收、分布、代谢和排泄)显示出相对理想的理化特性、药物相似性和药物化学性质。通过应用虚拟 Docker(Molegro MVD)对 3 个不同的人胰腺α-淀粉酶大分子靶标(PDB ID 1B2Y)、(PDB ID 3BAJ)和(PDB ID: 3OLI)进行分子对接分析,优化了这些主要候选药物的抗糖尿病作用。为便于比较,将二甲双胍作为参考标准。体外抗糖尿病评估结果显示,正己烷提取物的 IC50 值为 206.3 µM,乙酸乙酯提取物的 IC50 值为 41.64 µM,甲醇提取物的 IC50 值为 9.61 µM。根据硅学结果,所有 13 种植物成分都具有最佳的结合亲和力,其 MolDock 得分从 - 97.2003 到 - 65.6877 kcal/mol 不等,并且与原生药物二甲双胍相比,显示出大量的结合相互作用。因此,目前的研究得出的结论是,Fagonia indica 提取物和化合物的糖尿病抑制前景可能有助于作为一类新的抗糖尿病药物或类药物候选物进行进一步研究。
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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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