Theoretical investigation on known renin inhibitors and generation of ligand-based pharmacophore models for hypertension treatment

Abstract

The renin enzyme is considered a promising target for hypertension and renal diseases. Over the last three decades, several experimental and theoretical studies have been engaged in the discovery of potent renin inhibitors. The identified inhibitors that undergo clinical trials are still failing to meet the criteria of potency and safety. To date, there is no specific FDA-approved drug for renin inhibition. Our theoretical opinion describes that the most potent compounds identified in experimental studies but lacking safety and overdose issues could be solved by finding similar molecules that are stable, very active, and have no side effects, which will kick start the drug discovery process. Here, we utilized the most potent direct renin inhibitors reported earlier, followed further by our theoretical study reported in 2019. Ligand-based virtual screening, density functional theory, and dynamic simulation studies were employed to explore the identified compounds and co-crystallized molecule in the protein structure. From the diverse databases, we have identified several identical molecules based on their structural features, such as functional groups like hydrophobic (H), aromatic rings (R), hydrogen bond acceptor (A), and donor (D). The HHHPR five-point pharmacophore feature was identified as a template pharmacophore to search the potential compounds from the Enamine and LifeChemical databases and have a good fitness score with known renin inhibitors. Furthermore, theoretical validation was done through several studies that confirmed the activity of the identified molecules. Overall, we propose that these compounds might break the failure in adverse events and improve the potency of hypertension treatment.

Communicated by Ramaswamy H. Sarma

Keywords:

• Hypertension
• ligand-based drug design
• renin
• molecular dynamics
• pharmacophore
• direct renin inhibitors

Acknowledgements

The authors LL, JS and KM thankfully acknowledge the MHRD-RUSA 2.0 – F.24/51/2014-U Policy (TN Multi-Gen), Department of Education DST-FIST and DBT-Bioinformatics and Computational Biology Centre (BIC) for the infrastructure facilities.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings of this study are available within the article.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.