Dataset Information

QSAR and docking studies on capsazepine derivatives for immunomodulatory and anti-inflammatory activity.

ABSTRACT: Capsazepine, an antagonist of capsaicin, is discovered by the structure and activity relationship. In previous studies it has been found that capsazepine has potency for immunomodulation and anti-inflammatory activity and emerging as a favourable target in quest for efficacious and safe anti-inflammatory drug. Thus, a 2D quantitative structural activity relationship (QSAR) model against target tumor necrosis factor-? (TNF-?) was developed using multiple linear regression method (MLR) with good internal prediction (r2?=?0.8779) and external prediction (r2pred?=?0.5865) using Discovery Studio v3.5 (Accelrys, USA). The predicted activity was further validated by in vitro experiment. Capsazepine was tested in lipopolysaccharide (LPS) induced inflammation in peritoneal mouse macrophages. Anti-inflammatory profile of capsazepine was assessed by its potency to inhibit the production of inflammatory mediator TNF-?. The in vitro experiment indicated that capsazepine is an efficient anti-inflammatory agent. Since, the developed QSAR model showed significant correlations between chemical structure and anti-inflammatory activity, it was successfully applied in the screening of forty-four virtual derivatives of capsazepine, which finally afforded six potent derivatives, CPZ-29, CPZ-30, CPZ-33, CPZ-34, CPZ-35 and CPZ-36. To gain more insights into the molecular mechanism of action of capsazepine and its derivatives, molecular docking and in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were performed. The results of QSAR, molecular docking, in silico ADMET screening and in vitro experimental studies provide guideline and mechanistic scope for the identification of more potent anti-inflammatory & immunomodulatory drug.

SUBMITTER: Shukla A

PROVIDER: S-EPMC4086833 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

QSAR and docking studies on capsazepine derivatives for immunomodulatory and anti-inflammatory activity.

Shukla Aparna A Sharma Pooja P Prakash Om O Singh Monika M Singh Monika M Kalani Komal K Khan Feroz F Bawankule Dnyaneshwar Umrao DU Luqman Suaib S Srivastava Santosh Kumar SK

PloS one 20140708 7

Capsazepine, an antagonist of capsaicin, is discovered by the structure and activity relationship. In previous studies it has been found that capsazepine has potency for immunomodulation and anti-inflammatory activity and emerging as a favourable target in quest for efficacious and safe anti-inflammatory drug. Thus, a 2D quantitative structural activity relationship (QSAR) model against target tumor necrosis factor-α (TNF-α) was developed using multiple linear regression method (MLR) with good i ...[more]

PMID: 25003344

Similar Datasets

Project description:BackgroundInfluenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).ResultsThe 2D-QSAR modeling results showed GFA-MLR ( Rtrain2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, Rtest2 = 0.8943, Rpred2 = 0.7745) and GFA-ANN ( Rtrain2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, Rtest2 = 0.8831, Rpred2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( Rtrain2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( Rtrain2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> - 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.ConclusionThe drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Dataset Information

QSAR and docking studies on capsazepine derivatives for immunomodulatory and anti-inflammatory activity.

Publications

QSAR and docking studies on capsazepine derivatives for immunomodulatory and anti-inflammatory activity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets