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Targeting lipid-sensing nuclear receptors PPAR (α, γ, β/δ): HTVS and molecular docking/dynamics analysis of pharmacological ligands as potential pan-PPAR agonists

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Abstract

The global prevalence of obesity-related systemic disorders, including non-alcoholic fatty liver disease (NAFLD), and cancers are rapidly rising. Several of these disorders involve peroxisome proliferator-activated receptors (PPARs) as one of the key cell signaling pathways. PPARs are nuclear receptors that play a central role in lipid metabolism and glucose homeostasis. They can activate or suppress the genes responsible for inflammation, adipogenesis, and energy balance, making them promising therapeutic targets for treating metabolic disorders. In this study, an attempt has been made to screen novel PPAR pan-agonists from the ZINC database targeting the three PPAR family of receptors (α, γ, β/δ), using molecular docking and molecular dynamics (MD) simulations. The top scoring five ligands with strong binding affinity against all the three PPAR isoforms were eprosartan, canagliflozin, pralatrexate, sacubitril, olaparib. The ADMET analysis was performed to assess the pharmacokinetic profile of the top 5 molecules. On the basis of ADMET analysis, the top ligand was subjected to MD simulations, and compared with lanifibranor (reference PPAR pan-agonist). Comparatively, the top-scoring ligand showed better protein–ligand complex (PLC) stability with all the PPARs (α, γ, β/δ). When experimentally tested in in vitro cell culture model of NAFLD, eprosartan showed dose dependent decrease in lipid accumulation and oxidative damage. These outcomes suggest potential PPAR pan-agonist molecules for further experimental validation and pharmacological development, towards treatment of PPAR-mediated metabolic disorders.

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Abbreviations

PPAR:

Peroxisome proliferator-activated receptors

RXR:

Retinoid X receptor

NAFLD:

Non-alcoholic fatty liver disease

NASH:

Non-alcoholic steatohepatitis

HTVS:

High throughput virtual screening

MD:

Molecular dynamics

PDB:

Protein data bank

OPLS-2005:

Optimized potentials for liquid simulations

SP:

Standard precision

XP:

Extra precision

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Acknowledgements

SKM, SP and MMR acknowledged the Institute Fellowship from BITS Pilani, and BKK is grateful to the Ministry of Tribal Affairs, Government of India, for the research fellowship. SM acknowledges the research support from Department of Biotechnology (Indo-Spain Bilateral Program), Govt. of India, New Delhi. The authors thank Birla Institute of Technology and Science (BITS), Pilani – Pilani Campus for providing research support and computational infrastructure.

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Authors and Affiliations

  1. Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, Rajasthan, 333 031, India

    Sumit Kumar Mandal, Sonakshi Puri, Pankaj Kumar Sharma & P. R. Deepa

  2. Medicinal Chemistry Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, Rajasthan, 333 031, India

    Banoth Karan Kumar, Mohammed Muzaffar-Ur-Rehman & Murugesan Sankaranarayanan

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  1. Sumit Kumar Mandal
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Contributions

PRD, SM: Conceptualization; SKM, SP, BKK, MMR: Methodology & Experimentation; SKM, SP, BKK, MMR: Data curation Software; SKM, SP, BKK, & MMR: Writing and original draft preparation; SKM, SP, BKK, MMR, SM: Software and data validation; PRD, SM, PKS: Visualization, Investigation Supervision; SKM, SP, BKK, MMR, PRD, SM, PKS: Writing- Reviewing and Editing.

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Correspondence to P. R. Deepa.

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Mandal, S.K., Puri, S., Kumar, B.K. et al. Targeting lipid-sensing nuclear receptors PPAR (α, γ, β/δ): HTVS and molecular docking/dynamics analysis of pharmacological ligands as potential pan-PPAR agonists. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10666-y

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