Computational guided designing of novel Renin inhibitors

Keyword(s):  
Author(s):  
Shikha Sharma ◽  
Shweta Sharma ◽  
Vaishali Pathak ◽  
Parwinder Kaur ◽  
Rajesh Kumar Singh

Aim: To investigate and validate the potential target proteins for drug repurposing of newly FDA approved antibacterial drug. Background: Drug repurposing is the process of assigning indications for drugs other than the one(s) that they were initially developed for. Discovery of entirely new indications from already approved drugs is highly lucrative as it minimizes the pipeline of the drug development process by reducing time and cost. In silico driven technologies made it possible to analyze molecules for different target proteins which are not yet explored. Objective: To analyze possible targets proteins for drug repurposing of lefamulin and their validation. Also, in silico prediction of novel scaffolds from lefamulin has been performed for assisting medicinal chemists in future drug design. Methods: A similarity-based prediction tool was employed for predicting target protein and further investigated using docking studies on PDB ID: 2V16. Besides, various in silico tools were employed for prediction of novel scaffolds from lefamulin using scaffold hopping technique followed by evaluation with various in silico parameters viz., ADME, synthetic accessibility and PAINS. Results: Based on the similarity and target prediction studies, renin is found as the most probable target protein for lefamulin. Further, validation studies using docking of lefamulin revealed the significant interactions of lefamulin with the binding pocket of the target protein. Also, three novel scaffolds were predicted using scaffold hopping technique and found to be in the limit to reduce the chances of drug failure in the physiological system during the last stage approval process. Conclusion: To encapsulate the future perspective, lefamulin may assist in the development of the renin inhibitors and, also three possible novel scaffolds with good pharmacokinetic profile can be developed into both as renin inhibitors and for bacterial infections.


Virology ◽  
2021 ◽  
Vol 554 ◽  
pp. 48-54
Author(s):  
Rana H. Refaey ◽  
Mohamed K. El-Ashrey ◽  
Yassin M. Nissan

1988 ◽  
Vol 29 (37) ◽  
pp. 4665-4668 ◽  
Author(s):  
Dinesh V. Patel ◽  
Katherine Rielly-Gauvin ◽  
Denis E. Ryono

1989 ◽  
Vol 32 (7) ◽  
pp. 1652-1661 ◽  
Author(s):  
Mark C. Allen ◽  
Walter Fuhrer ◽  
Brian Tuck ◽  
Roy Wade ◽  
Jeanette M. Wood

1972 ◽  
Vol 15 (1) ◽  
pp. 58-60 ◽  
Author(s):  
Francis R. Pfeiffer ◽  
Clara K. Miao ◽  
Suzanne C. Hoke ◽  
Jerry A. Weisbach

1978 ◽  
Vol 298 (18) ◽  
pp. 1023-1025 ◽  
Author(s):  
Edgar Haber
Keyword(s):  

2021 ◽  
Author(s):  
P Ambili Unni ◽  
S Sajitha Lulu ◽  
Girinath G Pillai

<p>Hypertension is considered as the predominant risk factor for the onset of Cardiovascular disease (CVD) in the elder population. The chronic activation of Renin Angiotensin System (RAS) is considered as the primary causative factor for the inception of hypertension in geriatric population. Angiotensin Converting Enzyme (ACE) is a highly explored druggable target in the context of hypertension since this enzyme catalyses the conversion of angiotensin I to angiotensin II, a potent vasoconstrictor. But clinical trials conducted on ACE inhibitors reported their incompetence in the effective treatment of hypertension. Hence, recent studies are focussing on renin, which is a central component of RAS in the regulation of blood pressure. The present study focuses on the elucidation of physicochemical properties of chemical compounds essential for renin inhibition and identification of novel renin inhibitors possessing enhanced potency as well as bioavailability. We have employed Molecular Field Topology Analysis (MFTA) as well as Structure Based Drug Design (SBDD) approaches for the accomplishment of above-mentioned objectives. MFTA approach were piloted on 45 indole-3-carboxamide derivatives by elucidating the significance of charge distribution as well as molecular size of chemical species in eliciting renin inhibition. Optimal model was obtained with Nf = 3, r<sup>2 </sup>= 0.81 , Q<sup>2</sup> = 0.65. Molecular docking, atom-based binding free energy contributions and bioavailability assessments were carried out to identify most potent lead molecule among 45 compounds reported for renin inhibition. Further, new derivative molecules were predicted for the best lead molecule by employing chemical space exploration. All datasets, descriptor values, QSAR models for predictions usage and plots will be available in <a href="https://github.com/giribio/agingdata">https://github.com/giribio/agingdata</a></p><p></p>


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