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Exploring the therapeutic potential of leriodenine and nuciferine from Nelumbo nucifera for renal fibrosis: an In-silico analysis

  • Rushendran Rapuru , Rukaiah Fatma Begum EMAIL logo , S. Ankul Singh , Chitra Vellapandian EMAIL logo , Nemat Ali , Abdullah F. AlAsmari and Bhupendra G. Prajapati ORCID logo EMAIL logo
Published/Copyright: February 11, 2025
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Abstract

A major problem in chronic kidney illnesses is renal fibrosis. This research investigates the therapeutic potential of compounds derived from Nelumbo nucifera (Lotus). Comprehensive screening identified these compounds, which exhibit promising binding affinities with key targets associated with renal fibrosis. Leriodenine and Nuciferine demonstrate substantial potential by modulating critical targets such as PTGS2, JUN, EGFR, STAT3, mTOR, and AKT1. The identified biomolecule-target-pathway network highlights the intricate interactions underlying the therapeutic effects of lotus seed compounds in renal fibrosis. Strong binding affinities with PTGS2-PDBID:5F19, Leriodenine −8.99 kcal/mol and Nuciferine −9.33 kcal/mol, and JUN-PDBID:1S9K, Leriodenine −7.95 kcal/mol and Nuciferine −7.05 kcal/mol are shown by molecular docking investigations, indicating their potential as fibrotic process inhibitors. During 10 ns of molecular docking simulations, these compounds demonstrated robust hydrogen-bonding connections within the protein’s active site, leading to a possible alteration in the conformation of the ligand-binding site. The research establishes the foundation for future experimental validation, clinical trials, to bridge the translational gap. The research combines target prediction, protein–protein interaction studies, and biomolecular screening to clarify the molecular pathways behind renal fibrosis. We also carried out Insilico molecular docking and carried out molecular dynamics simulation of the best compound identified to obtain more precise results.


Corresponding authors: Rukaiah Fatma Begum, Institute of Pharmaceutical Research, GLA university, Mathura 281406, Uttar Pradesh, India, E-mail: ; Chitra Vellapandian, Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur 603 203, Chengalpattu, Tamil Nadu, India, E-mail: ; and Bhupendra G. Prajapati, Shree S. K. Patel College of Pharmaceutical Education and Research, Ganpat University, Kherva, Mahesana, 384012, Gujarat, India; and Faculty of Pharmacy, Silpakorn University, Sanam Chandra Palace Campus,6 Rajamankha Nai Road, Amphoe Muang, Nakhon Pathom Province 73000, Thailand, E-mail:

Award Identifier / Grant number: RSPD2025R940

Acknowledgments

The authors acknowledge and extend their appreciation to the Researchers Supporting Project Number (RSPD2025R940), King Saud University, Riyadh, Saudi Arabia for supporting this study. The authors collectively extend their appreciation to the administration of SRM College of Pharmacy, SRMIST, located in Kattankulathur, Chengalpattu, Tamil Nadu, India. Furthermore, R. R. expresses sincere gratitude to the Ministry of Tribal Affairs of the Indian government for the fellowship support extended. The fellowship is identified by the award number 202122-NFST-AND-00924. Dr. Prajapati extends his sincere appreciation to the Faculty of Pharmacy, Silpakorn University, Thailand, for their generous support that enabled the completion of this work.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: R, A.S.S, and R.F.B designed the study, collected and organized data, developed the methodology, and conducted preclinical research, securing funding; N.A, A.F.A created visual representations, performed formal analysis and interpretation, drafted the article, as extracted and compiled all the necessary data. C.V and B.G.P provided valuable supervision throughout the research process, contributing to data analysis, formal analysis, interpretation, validation, and data compilation. Lastly, all authors collectively reviewed and approved the article’s final version for publication.

  4. Use of Large Language Models, AI and Machine Learning Tools: Not applicable.

  5. Conflict of interest: The authors affirm that they do not have any conflicts of interest to disclose.

  6. Research funding: The research was funded through the Researchers Supporting Project Number (RSPD2024R940), King Saud University, Riyadh, Saudi Arabia.

  7. Data availability: The authors do not have additional generated data to disclose.

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Received: 2024-10-16
Accepted: 2025-01-18
Published Online: 2025-02-11

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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