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In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive
Miah Roney1,2, Md Nazim Uddin3, Azmat Ali Khan4
1Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Persiaran Tun Khalil Yaakob, Gambang, 26300 Kuantan, Pahang Malaysia.
Abstract:
Prostate cancer (PCa) is the second most frequent and the fifth greatest cause of death in men. Although there are already therapies for early-stage PCa, their effectiveness in advanced PCa is limited, primarily because of medication resistance or poor efficacy. To find new therapeutic indications or repurpose current medications, this project intends to use computational approaches to investigate possible anti-PCa compounds based on simulated screening of FDA-approved drug bank databases. The techniques used in this study include virtual screening of the drug bank database utilising a matrix or user molecule (Diffractaic acid; DA), integrated network pharmacology, molecular docking, detailed molecular dynamic simulation. The results showed that 18 DA analogues in total were chosen from the drug bank database and put through integrated network pharmacology. The KEGG enrichment analysis indicated that hsa05215: Prostate cancer is one of the most significant PCa enrichment signalling pathways which exhibited 18 protein-protein interactions including PDGFRB, PDGFRA, PPARG, GSK3B, MAP2K1, CREBBP, HSP90AA1, HSP90AB1, CHUK, PIK3CD, BRAF, PIK3CB, MTOR, AR, PIK3CA, PLAU, CDK2, and MAPK1. Subsequent molecular docking study with the best target protein (PPARγ) showed that the DB14929 molecule formed hydrogen bonds with the Gln273, Arg280, Arg288, and Ser342 residues and exhibited a high binding affinity ( - 10.5 kcal/mol) for the PPARγ agonist against PCa. Furthermore, molecular dynamic simulation showed that DB14929 formed a stable protein-ligand complex with RMSD, RMSF, Rg, and SASA values. Furthermore, the dynamic behaviour of the PPARγ protein linked to DB14929 in its conformational space was analysed using the PCA technique, demonstrating the excellent conformational space behaviour. Additionally, the free binding energy value of - 57.15 kcal/mol of DB14929 indicated that it could be an agonist of PPARγ of PCa.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s13205-025-04376-5.
Insights
Computational screening identified DB14929 as a potential prostate cancer (PCa) drug. This Diffractaic acid analogue shows promise as a PPARγ agonist, offering new therapeutic avenues for advanced PCa.
Area of Science:
- Oncology
- Computational Chemistry
- Pharmacology
Background:
- Prostate cancer (PCa) poses a significant global health challenge, with limited treatment options for advanced stages due to drug resistance.
- Existing therapies for early-stage PCa often show reduced efficacy in advanced disease, necessitating novel therapeutic strategies.
- Drug repurposing and discovery of new anti-PCa compounds are critical for improving patient outcomes.
Purpose of the Study:
- To computationally screen FDA-approved drug banks for potential anti-prostate cancer (PCa) compounds.
- To identify novel therapeutic agents or repurpose existing drugs for advanced PCa treatment.
- To investigate the efficacy of Diffractaic acid (DA) analogues as potential PCa therapeutics.
Main Methods:
- Virtual screening of a drug bank database using Diffractaic acid (DA) as a reference molecule.
- Integrated network pharmacology and KEGG enrichment analysis to identify key signaling pathways.
- Molecular docking, molecular dynamic simulations, and principal component analysis (PCA) to assess binding affinity and stability.
Main Results:
- Eighteen Diffractaic acid (DA) analogues were selected, with KEGG analysis highlighting the prostate cancer pathway (hsa0215).
- The molecule DB14929 demonstrated high binding affinity (-10.5 kcal/mol) to PPARγ, a key target in PCa.
- Molecular dynamics simulations confirmed a stable protein-ligand complex for DB14929 and PPARγ, with a binding energy of -57.15 kcal/mol.
Conclusions:
- DB14929 shows significant potential as a PPARγ agonist for treating prostate cancer (PCa).
- The computational approach successfully identified a promising drug candidate for further preclinical investigation.
- This study provides a foundation for developing new therapeutic strategies against advanced PCa.
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