Insilico studies, synthesis and antitubercular activity of some novel quinoline – azitidinone derivatives

Background: Diarylquinolines like Bedaquiline have shown promising antitubercular activity by their action of Mycobacterial ATPase. Objective: The structural features necessary for good antitubercular activity for a series of quinoline derivatives were explored through computational chemistry tools like QSAR and combinatorial library generation. In the current study, 3-Chloro-4-(2-mercaptoquinoline-3-yl)-1-substitutedphenylazitidin-2-one derivatives have been designed and synthesized based on molecular modeling studies as anti-tubercular agents. Method: 2D and 3DQSAR analysis was used to designed compounds having quinoline scaffold. The synthesized compounds were evaluated against active and dormant strains of Mycobacterium tuberculosis (MTB) H37 Ra and Mycobacterium bovis BCG. The compounds were also tested for cytotoxicity against MCF-7, A549 and Panc-1 cell lines using MTT assay. Binding affinity of designed compounds was gauged by molecular docking studies. Results: Statistically significant QSAR models generated by SA-MLR method for 2D QSAR exhibited r2 = 0.852, q2 = 0.811and whereas 3D QSAR with SA-kNN showed q2 = 0.77. The synthesized compounds exhibited MIC in the range of 1.38-14.59(µg/ml) .These compounds showed some crucial interaction with MTB Atpase. Conclusion: The present study has shown some promising results which can be further explored for lead generation.

Download Full-text

2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2017010104 ◽

2017 ◽

Vol 2 (1) ◽

pp. 44-63

Author(s):

Anacleto S. de Souza ◽

Leonardo G. Ferreira ◽

Adriano D. Andricopulo

Keyword(s):

Neglected Tropical Diseases ◽

Public Health Problem ◽

Predictive Ability ◽

3D Qsar ◽

Structural Features ◽

Field Analysis ◽

Global Public Health ◽

Qsar Studies ◽

Qsar Models ◽

2D And 3D

Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.

Download Full-text

Identifying the structural features and diversifying the chemical domain of peripherally acting CB1 receptor antagonists using molecular modeling techniques

RSC Advances ◽

10.1039/c5ra20612j ◽

2016 ◽

Vol 6 (2) ◽

pp. 1466-1483 ◽

Cited By ~ 5

Author(s):

Mayank Kumar Sharma ◽

Prashant R. Murumkar ◽

Guanglin Kuang ◽

Yun Tang ◽

Mange Ram Yadav

Keyword(s):

Molecular Modeling ◽

Virtual Screening ◽

3D Qsar ◽

Structural Features ◽

Cb1 Receptor ◽

Receptor Antagonists ◽

Modeling Techniques ◽

Qsar Models ◽

Cb1 Receptor Antagonists

A four featured pharmacophore and predictive 3D-QSAR models were developed which were used for virtual screening of the Asinex database to get chemically diverse hits of peripherally active CB1 receptor antagonists.

Download Full-text

Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

Canadian Journal of Chemistry ◽

10.1139/v2012-047 ◽

2012 ◽

Vol 90 (8) ◽

pp. 675-692 ◽

Cited By ~ 6

Author(s):

Premlata K. Ambre ◽

Raghuvir R. S. Pissurlenkar ◽

Evans C. Coutinho ◽

Radhakrishnan P. Iyer

Keyword(s):

Hydrogen Bond ◽

3D Qsar ◽

Pharmacophore Modeling ◽

Docking Studies ◽

Hydrogen Bond Donor ◽

Hydrogen Bond Acceptor ◽

Checkpoint Kinase ◽

Checkpoint Kinase 1 ◽

Qsar Models ◽

Potential Inhibitors

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

Download Full-text

In silico studies on smoothened human receptor and its antagonists in search of anticancer effects

Journal of the Serbian Chemical Society ◽

10.2298/jsc190403085b ◽

2020 ◽

Vol 85 (3) ◽

pp. 335-346

Author(s):

Ana Borota ◽

Sorin Avram ◽

Ramona Curpan ◽

Alina Bora ◽

Daniela Varga ◽

...

Keyword(s):

Acquired Resistance ◽

Area Under The Curve ◽

Weighted Average ◽

3D Qsar ◽

Qsar Model ◽

Docking Studies ◽

Receptor Inhibition ◽

In Silico Studies ◽

Qsar Models ◽

Pharmacophore Hypothesis

Lately, the cancers related with abnormal hedgehog (Hh) signalling pathway are targeted by smoothened (SMO) receptor inhibitors that are rapidly developing. Still, the problems of known inhibitors such as severe side effects, weak potency against solid tumors or even the acquired resistance need to be overcome by developing new suitable inhibitors. To explore the structural requirements of antagonists needed for SMO receptor inhibition, pharmacophore mapping, 3D-QSAR models, database screening and docking studies were performed. The best selected pharmacophore hypothesis based on which statistically significant atom-based 3D-QSAR model was developed (R2 = = 0.856, Q2 = 0.611 and Pearson-R = 0.817), was further subjected to dataset screening in order to evaluate its ability to prioritize active compounds over decoys. The efficiency of one four-points pharmacophore hypothesis (AAHR.524) was observed based on good evaluation metrics such as the area under the curve (0.795), and weighted average precision (0.835), suggesting that the model is trustworthy in predicting novel inhibitors against SMO receptor.

Download Full-text

2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

Pharmaceutical Sciences ◽

10.4018/978-1-5225-1762-7.ch037 ◽

2017 ◽

pp. 956-977

Author(s):

Anacleto S. de Souza ◽

Leonardo G. Ferreira ◽

Adriano D. Andricopulo

Keyword(s):

Neglected Tropical Diseases ◽

Public Health Problem ◽

Predictive Ability ◽

3D Qsar ◽

Structural Features ◽

Field Analysis ◽

Global Public Health ◽

Qsar Studies ◽

Qsar Models ◽

2D And 3D

Download Full-text

QSAR and Structure-Based Docking Studies of Aryl Pyrido[2,3-d]pyrimidin-7(8H)-ones

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2018010103 ◽

2018 ◽

Vol 3 (1) ◽

pp. 43-73 ◽

Cited By ~ 1

Author(s):

Jyoti Durgapal ◽

Neha Bisht ◽

Muneer Alam ◽

Dipiksha Sharma ◽

Mohd Salman ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Model Validation ◽

Biological Activities ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Structural Features ◽

Docking Studies ◽

Linear Regression Method ◽

Qsar Models

The target of the present study has been to carry out computer-aided anticancer drug design utilizing genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure activity relationship (QSAR) of fibroblast growth factor (FGFr) inhibition of pyrido[2,3-d]pyrimidine-7(8H)-one compounds utilizing different classes of computed structural descriptors. A QSAR model was developed utilizing a combination of constitutional, functional group, geometrical and atom-centered fragment indices by multiple linear regression method and the model validation was performed by searching the predictability of the QSAR models. After outlier analyses through applicability domain, the model validation results were improved. In this connection, molecular docking studies were performed to predict the mode of binding and important structural features necessary for producing biological activities. This attempt could be helpful for further modeling of potent less toxic anticancer chemotherapeutics in these congeners.

Download Full-text

Identifying the Structural Features of Diphenyl Ether Analogues for InhA Inhibition: A 2D and 3D QSAR Based Study

Letters in Drug Design & Discovery ◽

10.2174/1570180816666190611153933 ◽

2019 ◽

Vol 17 (1) ◽

pp. 31-47 ◽

Cited By ~ 2

Author(s):

Ashutosh Prasad Tiwari ◽

Varadaraj Bhat Giliyar ◽

Gurypur Gautham Shenoy ◽

Vandana Kalwaja Eshwara

Keyword(s):

Inhibitory Activity ◽

Diphenyl Ether ◽

3D Qsar ◽

Qsar Model ◽

Structural Features ◽

Mycolic Acids ◽

2D Qsar ◽

Qsar Models ◽

Mycobacterial Cell Wall ◽

2D And 3D

Background: Enoyl acyl carrier protein reductase (InhA) is a validated target for Mycobacterium. It is an enzyme which is associated with the biosynthesis of mycolic acids in type II fatty acid synthase system. Mycobacterial cell wall majorly comprises mycolic acids, which are responsible for virulence of the microorganism. Several diphenyl ether derivatives have been known to be direct inhibitors of InhA. Objective: In the present work, a Quantitative Structure Activity Relationship (QSAR) study was performed to identify the structural features of diphenyl ether analogues which contribute to InhA inhibitory activity in a favourable way. Method: Both 2D and 3D QSAR models were built and compared. Several fingerprint based 2D QSAR models were generated and their relationship with the structural features was studied. Models which corroborated the inhibitory activity of the molecules with their structural features were selected and studied in detail. Results: A 2D-QSAR model, with dendritic fingerprints having regression coefficient, for test set molecules Q2 =0.8132 and for the training set molecules, R2 =0.9607 was obtained. Additionally, an atom-based 3D-QSAR model with Q2 =0.7697 and R2 =0.9159 was also constructed. Conclusion: The data reported by various models provides guidance for the designing of structurally new diphenyl ether inhibitors with potential activity against InhA of M. tuberculosis.

Download Full-text

QSAR and Docking Studies on Piperidyl-cyclohexylurea Derivatives for Prediction of Selective and Potent Inhibitor of Matriptase

Current Computer - Aided Drug Design ◽

10.2174/1573409914666180516162349 ◽

2019 ◽

Vol 15 (2) ◽

pp. 167-181 ◽

Cited By ~ 1

Author(s):

Agha Zeeshan Mirza ◽

Hina Shamshad

Keyword(s):

Active Compound ◽

Potent Inhibitor ◽

3D Qsar ◽

Qsar Model ◽

Docking Studies ◽

Lack Of Fit ◽

Hydrogen Donors ◽

Qsar Models ◽

Test Sets ◽

New Compounds

Background: QSAR models as PLS, GFA, and 3D were developed for a series of matriptase inhibitors using 35 piperidyl-cyclohexylurea compounds. The training and test sets were divided into a set of 28 and 8 compounds, respectively and the pki values of each compound were used in the analysis. Methods: Docking and alignment methodologies were used to develop models in 3D QSAR. The best models among all were selected on the basis of regression statistics as r2, predictive r2 and Friedman Lack of fit measure. Hydrogen donors and rotatable bonds were found to be positively correlated properties for this target. The models were validated and used for the prediction of new compounds. Based on the predictions of 3D-QSAR model, 17 new compounds were prepared and their activities were predicted and compared with the active compound. Prediction of activities was performed for these 18 compounds using consensus results of all models. ADMET was also performed for the best-chosen compound and compared with the known active. Results and Conclusion: The developed model was able to validate the obtained results and can be successfully used to predict new potential and active compounds.

Download Full-text