{"title":"Antidiabetic Advancements In Silico: Pioneering Novel Heterocyclic\nDerivatives through Computational Design","authors":"Anuradha Mehra, Aryan Mehra","doi":"10.2174/0115743624282326240418104054","DOIUrl":null,"url":null,"abstract":"\n\nDeficiency of insulin signaling in type 2 diabetes results from insulin\nresistance or defective insulin secretion and induced hyperglycemia. By reducing glycated hemoglobin,\nSGLT2 inhibitors improve hyperuricemia, blood lipids, and weight loss without increasing\nthe risk of hypoglycemia. By targeting this pathway, SGLT2 inhibitors can become a\nprominent target in the management of type 2 diabetes.\n\n\n\nThis study aimed to carry out the molecular docking and ADMET prediction of novel\nimidazo(2,1-b)-1,3,4 thiadiazole derivatives as SGLT2 inhibitors.\n\n\n\nThe chemical structures of 108 molecules were drawn by using ChemDraw Professional\n15.0. Further, their energy minimization was also carried out by using Chem Bio Draw\nthree-dimensional (3D) Ultra 12.0. Molecular docking was also carried out using a Molegro Virtual\nDocker to identify the best-fitting molecules and to identify the potential leads on the basis\nof dock score. The predicted parameters of drug-likeness according to Lipinski’s rule of five,\nsuch as molecular weight, log P, hydrogen bond acceptor, hydrogen bond donors, and number of\nrotatable bonds of the selected compounds, were predicted using pKCSM software.\n\n\n\nAbout 108 molecules were designed by employing different substitutions on imidazothiadiazole\nnucleus as SGLT2 inhibitors. Out of these, 10 compounds were found to have better\ninteractions with the active site of SGLT2 protein and the highest dock scores compared to\ncanagliflozin. Compounds 39a and 39b demonstrated good interactions and the highest docking\nscores of -155.428 and -142.786, respectively. The in silico physicochemical properties of the\nbest compounds were also determined. Additionally, these compounds suggested a good pharmacokinetic\nprofile as per Lipinski's rule of five (orally active drugs).\n\n\n\nNovel imidazo (2,1-b)-1,3,4 thiadiazole derivatives were strategically designed,\nand their binding affinity was meticulously evaluated against the SGLT2 protein. This endeavor\nyielded pioneering lead compounds characterized by ultimate binding affinity, coupled with optimal\nADMET properties in adherence to Lipinski's rule of five and favourable noncarcinogenic\nprofile.\n","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"60 23","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Signal Transduction Therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115743624282326240418104054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Deficiency of insulin signaling in type 2 diabetes results from insulin
resistance or defective insulin secretion and induced hyperglycemia. By reducing glycated hemoglobin,
SGLT2 inhibitors improve hyperuricemia, blood lipids, and weight loss without increasing
the risk of hypoglycemia. By targeting this pathway, SGLT2 inhibitors can become a
prominent target in the management of type 2 diabetes.
This study aimed to carry out the molecular docking and ADMET prediction of novel
imidazo(2,1-b)-1,3,4 thiadiazole derivatives as SGLT2 inhibitors.
The chemical structures of 108 molecules were drawn by using ChemDraw Professional
15.0. Further, their energy minimization was also carried out by using Chem Bio Draw
three-dimensional (3D) Ultra 12.0. Molecular docking was also carried out using a Molegro Virtual
Docker to identify the best-fitting molecules and to identify the potential leads on the basis
of dock score. The predicted parameters of drug-likeness according to Lipinski’s rule of five,
such as molecular weight, log P, hydrogen bond acceptor, hydrogen bond donors, and number of
rotatable bonds of the selected compounds, were predicted using pKCSM software.
About 108 molecules were designed by employing different substitutions on imidazothiadiazole
nucleus as SGLT2 inhibitors. Out of these, 10 compounds were found to have better
interactions with the active site of SGLT2 protein and the highest dock scores compared to
canagliflozin. Compounds 39a and 39b demonstrated good interactions and the highest docking
scores of -155.428 and -142.786, respectively. The in silico physicochemical properties of the
best compounds were also determined. Additionally, these compounds suggested a good pharmacokinetic
profile as per Lipinski's rule of five (orally active drugs).
Novel imidazo (2,1-b)-1,3,4 thiadiazole derivatives were strategically designed,
and their binding affinity was meticulously evaluated against the SGLT2 protein. This endeavor
yielded pioneering lead compounds characterized by ultimate binding affinity, coupled with optimal
ADMET properties in adherence to Lipinski's rule of five and favourable noncarcinogenic
profile.
期刊介绍:
In recent years a breakthrough has occurred in our understanding of the molecular pathomechanisms of human diseases whereby most of our diseases are related to intra and intercellular communication disorders. The concept of signal transduction therapy has got into the front line of modern drug research, and a multidisciplinary approach is being used to identify and treat signaling disorders.
The journal publishes timely in-depth reviews, research article and drug clinical trial studies in the field of signal transduction therapy. Thematic issues are also published to cover selected areas of signal transduction therapy. Coverage of the field includes genomics, proteomics, medicinal chemistry and the relevant diseases involved in signaling e.g. cancer, neurodegenerative and inflammatory diseases. Current Signal Transduction Therapy is an essential journal for all involved in drug design and discovery.