ATP Synthase Inhibitors as Anti-tubercular Agents: QSAR Studies in Novel Substituted Quinolines

2020 ◽  
Vol 20 (29) ◽  
pp. 2723-2734
Author(s):  
Anil K. Saxena ◽  
Muneer Alam
Keyword(s):  
Atp Synthase ◽  
Antitubercular Activity ◽  
Pharmacophore Modeling ◽  
World Health ◽  
Data Set ◽  
Antitubercular Drugs ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Substituted Quinolines ◽  
Qsar Models

Background: Tuberculosis (TB) is a major infectious disease caused by Mycobacterium Tuberculosis. As per the World Health Organization (WHO) report of 2019, there were 1.5 million deaths in the year 2018, mainly because of multi- and extensively drug-resistant tuberculosis (MDR & XDR-TB). Among several antitubercular drugs in clinical trials, bedaquiline (TMC207) is a highly promising drug that was approved by the FDA in 2012 and marketed in 2016 for the treatment of multidrug resistant TB in combination with other drugs. Bedaquiline acts on mycobacterial ATP synthase and is highly effective in replicating as well as on dormant mycobacteria. Several series of substituted quinolines have been reported with their antitubercular and ATP synthase inhibitory activity. Methods: To understand the role of physicochemical parameters like hydrophobicity, electronic and steric factors in eliciting the biological response, the Quantitative structure-activity relationship (QSAR) studies have been carried out using the computed parameters as independent variable and activity (-log IC50/MIC) as the dependent variable. Results: The developed QSAR models in terms of positively contributing Molar Refractivity (MR) and negatively contributing Partition Coefficient (PC) and Connolly Molecular Area (CMA) parameters have high predictivity as also shown on external data set and the mean value of the computed 3D parameters of enantiomers may be used in QSAR analysis for racemic compounds. Conclusion: These results are also substantiated by pharmacophore modeling. The similar dependence of antitubercular activity against whole-cell M.Tb.H37Rv on MR and CMA suggests ATP synthase as the main target for antitubercular activity and the QSAR models may be useful in the identification of novel antitubercular agents.

A Comparative Study of 1D Descriptors Supported CoMFA and CoMSIA QSAR Models to Gain Novel Insights into 1,2,4-Triazoles Acting As Antitubercular Agents

2020 ◽  
Vol 16 ◽  
Author(s):  
Rajdeep Ray ◽  
Gautham Shenoy ◽  
N V Ganesh Kumar Tummalapalli
Keyword(s):  
External Validation ◽  
Antitubercular Activity ◽  
New Drugs ◽  
Rational Approach ◽  
Antitubercular Drugs ◽  
Comfa Model ◽  
Qsar Models ◽  
Structural Insights ◽  
The Way

: Tuberculosis is one of the leading cause for deaths due to infectious disease worldwide. There is an urgent need for developing new drugs due to the rising incidents of drug resistance. Triazoles have previously been reported to show antitubercular activity. Various computational tools pave the way for a rational approach in understanding the structural importance of these compounds in inhibiting Mycobacterium tuberculosis growth. The aim of this study is to develop and compare two different QSAR models based on a set of previously reported molecules and use the best one for gaining structural insights in to the Triazole molecules. In the current study, two separate models were generated with CoMFA and CoMSIA descriptors respectively based on a dataset of triazole molecules showing antitubercular activity. Several one dimensional (1D) descriptors were added to each of the models and the validation results and the contour data generated from them were compared. The best model was studied to give a detailed understanding of the triazole molecules and their role in the antitubercular activity.The r2, q2, predicted r2 and SEP (Standard error of prediction) for the CoMFA model were 0.866, 0.573, 0.119 and 0.736 respectively and for the CoMSIA model the r2, q2, predicted r2 and SEP were calculated to be 0.998, 0.634, 0.013 and 0.869 respectively. Although both the QSAR models produced acceptable internal and external validation scores but the CoMSIA results were significantly better. The CoMSIA contours also provided a better match than CoMFA with most of the features of the active compound 30b. Hence, the CoMSIA model was chosen and its contours were explored for gaining structural insights on the triazole molecules. The CoMSIA contours helped us to understand the role of several atoms and groups of the triazole molecules in their biological activity. The possibilities for substitution in the triazole compounds that would enhance the activity were also analysed. Thus, this study paves the way for designing new antitubercular drugs in future.

QSAR Analysis of Saquinavir Analogues (Peptidomimetics) as Potent HIV‐Protease Inhibitors

2012 ◽  
Vol 2 (3) ◽  
pp. 118-127
Author(s):  
Vandana Saini ◽  
Ajit Kumar
Keyword(s):  
Biological Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Partial Least Square ◽  
Least Square ◽  
Activity Prediction ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Physicochemical Descriptors ◽  
Qsar Models

The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D‐ and 3D‐ Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIV‐protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), k‐nearest neighbour (k‐NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2D‐QSAR analysis. In case of 3D‐QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3D‐QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIV‐protease. Also, the QSAR models generated can be very useful to predict the HIV‐PIs and also for virtual screening for identification of new lead molecules.

scholarly journals 2-Amino-4-arylthiazole Derivatives as Anti-giardial Agents: Synthesis, Biological Evaluation and QSAR Studies

2015 ◽  
Vol 13 (1) ◽  
Author(s):  
Raul Mocelo-Castell ◽  
Carlos Villanueva-Novelo ◽  
David Cáceres-Castillo ◽  
Ruben M. Carballo ◽  
Ramiro F. Quijano-Quiñones ◽  
...  
Keyword(s):  
Positive Control ◽  
Biological Evaluation ◽  
Toxicity Tests ◽  
Data Set ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Modified Method ◽  
Acute Toxicity Tests ◽  
Low Toxicity

AbstractA series of seven 2-amino-4-arylthiazoles were prepared following Hantzsch’s modified method under microwave irradiation. A set of 50 derivatives was obtained and the in vitro activity against Giardia intestinalis was evaluated. The results on the biological activity revealed that, in general, the N-(5-bromo-4-aryl-thiazol-2-yl)-acetamide scaffold showed high bioactivity. In particular, compounds 6e (IC50 = 0.39 μM) and 6b (IC50 = 0.87 μM) were found to be more potent than the positive control metronidazole. Citoxicity and acute toxicity tests performed showed low toxicity and high selectivity of the most active compounds (6e SI = 139, 6b SI = 52.3). A QSAR analysis was applied to a data set of 37 obtained 2-amino-4-arylthiazoles derivatives and the best model described a strongly correlation between the anti-giardiasic activity and molecular descriptors as E2M, RDF115m, F10, MATS6v, and Hypnotic-80, with high statistical quality. This finding indicates that N-substituted aminothiazole scaffold should be investigated for the development of highly selective anti-giardial agent.

Synthesis, Antitubercular Activity, Molecular Modeling and Docking Studies of Novel Thiazolidin-4-One Linked Dinitrobenzamide Derivatives

2020 ◽  
Vol 16 (1) ◽  
pp. 64-71
Author(s):  
Karanveer Singh ◽  
Manish Sinha ◽  
Shruti Kuletha ◽  
Baljeet Kaur ◽  
Amandeep Kaur ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Molecular Modeling ◽  
Antitubercular Activity ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Lead Optimization ◽  
World Health ◽  
Aromatic Moiety ◽  
Hydrogen Bond Interactions ◽  
The World

Background: Tuberculosis is a catastrophe sprawled across the world. The World Health Organization Global Tuberculosis Report 2017 inferred that there were an estimated 10.4 million people suffered from tuberculosis including 490000 Multidrug-Resistant TB (MDR-TB) cases. Several new lead molecules like dinitrobenzamide derivatives were found to be highly active against multidrugresistant strains of M. tuberculosis. To further explore the pharmacophoric space around the dinitobenzamide moiety, a series of compounds have been synthesized by linking it with the thiazolidin- 4-one. The presented work is an effort to study the biological effect of thiazolidin-4-one scaffold on dinitrobenzamide derivatives as antitubercular agents. A molecular modeling study was also performed on the synthesized molecules to reveal the requirements for further lead optimization. Methods: The thiazolidin-4-one linked 3,5-dinitrobenzamide derivatives have been synthesized by onepot three-component condensation reaction of an amine, substituted aldehydes and thioglycolic acid in presence of N, N'-Dicyclohexylcarbodiimide (DCC). These compounds were evaluated against Mycobacterium tuberculosis H37Ra. A pharmacophore modeling approach has been used in order to explore the collection of possible pharmacophore queries of thiazolidin-4-one linked 3, 5-dinitrobenzamide derivatives against M. tuberculosis. The synthesized compounds were docked on to the M. tuberculosis DprE1 enzyme to identify the structural features requirement of these analogs against this potential target of M. tuberculosis. Results: The synthesized compounds showed the antitubercular activity in the range of 6.25-50 μg/ml. The pharmacophore modeling suggests that the presence of aromatic moiety, thiazolidin-4-one ring and one of the nitro groups are significant for inhibiting the enzymatic activity. While docking studies showed that hydrophobic and hydrogen bond interactions of the aromatic moiety and nitro group crucial to inactivate the DprE1 enzyme. Conclusion: The study showed that the linking of thiazolidin-4-one with dinitrobenzamide leads to compounds active against M. tuberculosis. These findings also suggested that further lead optimization would be carried out by focusing on the aromatic system along with electron-rich substituents placed on the thiazolidin-4-one for making better hydrophobic and hydrogen bond interactions with the DprE1 target.

DESIGN OF NEW PEROXISOME PROLIFERATORS GAMMA ACTIVATED RECEPTOR AGONISTS (PPARγ) VIA QSAR BASED MODELING

2018 ◽  
pp. 23-26 ◽  
Author(s):  
Tripathi RB ◽  
Jain J ◽  
Siddiqui AW
Keyword(s):  
Molecular Descriptor ◽  
Binding Pocket ◽  
Peroxisome Proliferators ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Artery Disease ◽  
Peroxisome Proliferators Activated Receptors ◽  
Recent Attention

The Peroxisome proliferators-activated receptors (PPARs) are one of the nuclear fatty acid receptors, which contain a type II zincfinger DNA binding pattern and a hydrophobic ligand binding pocket. These receptors are thought to play an essential role in metabolic diseasessuch as obesity, insulin resistance, and coronary artery disease. Therefore Peroxisome Proliferators-Activated Receptor (PPARγ) activators havedrawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize newPPARγ activators. Objective: The aim of the study was to finding new selective human PPARγ (PPARγ) modulators that are able to improveglucose homeostasis with reduced side effects compared with TZDs and identify the specific molecular descriptor and structural constraint toimprove the agonist activity of PPARγ analogs. Material and Method: Software’s that was used for this study include S.P. Gupta QSARsoftware (QSAR analysis), Valstat (Comparative QSAR analysis and calculation of L-O-O, Q2, r2, Spress), BILIN (Comparative QSAR analysisand calculation of Q2, r, S, Spress, and F), etc., allowing directly performing statistical analysis. Then multiple linear regression based QSARsoftware (received from BITS-Pilani, India) generates QSAR equations. Result and Discussion: In this study, we explored the quantitativestructure–activity relationship (QSAR) study of a series of meta-substituted Phenyl-propanoic acids as Peroxisome Proliferators Gamma activatedreceptor agonists (PPARγ).The activities of meta-substituted Phenyl-propanoic acids derivatives correlated with various physicochemical, electronic and steric parameters.Conclusion: The identified QSAR models highlighted the significance of molar refractivity and hydrophobicity to the biological activity.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds

2020 ◽  
Vol 17 (2) ◽  
pp. 214-225 ◽  
Author(s):  
Piotr Kawczak ◽  
Leszek Bober ◽  
Tomasz Bączek
Keyword(s):  
Antimicrobial Activity ◽  
Heterocyclic Compounds ◽  
Molecular Descriptors ◽  
Microbiological Activity ◽  
Activity Data ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Nitro Derivatives ◽  
Semi Empirical ◽  
Derivatives Of

Background: Nitro-derivatives of heterocyclic compounds were used as active agents against pathogenic microorganisms. A set of 4- and 5-nitroimidazole derivatives exhibiting antimicrobial activity was analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. The study included compounds used both in documented treatment and those described as experimental. Objective: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. Methods: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical and ab initio level of in silico molecular modeling was performed for calculations of molecular descriptors. Results: QSAR models were proposed based on chosen descriptors. The relationship between the nitro-derivatives structure and microbiological activity data was able to class and describe the antimicrobial activity with the use of statistically significant molecular descriptors. Conclusion: The applied chemometric approaches revealed the influential features of the tested structures responsible for the antimicrobial activity of studied nitro-derivatives.

Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking

2019 ◽  
Vol 15 (4) ◽  
pp. 328-340 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Napat Songtawee ◽  
Suphakit Siriwong ◽  
Supaluk Prachayasittikul ◽  
Chanin Nantasenamat ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Rational Design ◽  
External Validation ◽  
Rational Drug Design ◽  
Support Vector ◽  
Data Set ◽  
Qsar Models ◽  
Anti Hiv Agents ◽  
Anti Hiv ◽  
Colchicine Derivatives

Background: Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required. Objective: This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity. Methods: A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking. Results: The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO−CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed. Conclusion: These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.

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