Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets.
{"title":"Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets.","authors":"Rabia Riaz, Shagufta Parveen, Nusrat Shafiq, Awais Ali, Maryam Rashid","doi":"10.1007/s11030-024-10897-7","DOIUrl":null,"url":null,"abstract":"<p><p>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 IC<sub>50</sub> 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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Diversity","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s11030-024-10897-7","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0
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.
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;