Nature’s answer to thyroid cancer: unraveling eclipta prostrata’s hidden potential through <i>in silico</i> molecular exploration
Thyroid cancer (TC) is poised to become the fourth most prevalent global malignancy, highlighting the urgency for innovative therapeutic strategies. Current treatments often come with significant adverse effects, emphasizing the necessity for targeted drugs with reduced side effects. In this study, we have employed a comprehensive approach, including network pharmacology, molecular docking, molecular dynamics simulations, and MMPBSA to uncover the potential compounds within <i>Eclipta prostrata</i> and their interactions with SRC kinase of TC. The Src family of kinases is a versatile family of nonreceptor tyrosine kinase, regulating key cellular processes responsible for mediating thyroid tumor progression. Our aim is to harness nature’s arsenal for the development of safe and effective TC therapeutics. We have identifyied six <i>E. prostrata</i> compounds, carefully chosen based on Lipinski’s Rule of Five, bioavailability and drug-likeness scores. Additionally, our research pinpoints the top three core proteins through network pharmacology, assessing their potential for immune infiltration into cancer tissue and examining mRNA expression profiles across various pathological stages. Significantly, luteolin emerged as a primary SRC kinase inhibitor, displaying remarkable binding energy of −9.0 kcal/mol, comparable to the standard quinazoline inhibitor with binding energy of −9.5 kcal/mol. In addition, rigorous 100 nanosecond molecular dynamics simulations were performed on protein-compound complex, confirming their thermodynamic stability. Additionally, MM-PBSA also confirmed the docking results. Our in silico analysis suggested that luteolin holds promise as potent TC inhibitor, ushering in a new era in the quest for natural, low-risk therapeutic options for thyroid cancer, inspiring hope for patients and healthcare providers alike.
Publication Details
- Journal
- Figshare
- Published
- 2025-01-01
- DOI
- https://doi.org/10.6084/m9.figshare.28675954
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00