In Silico Analysis of Phytochemicals from Catharanthus roseus Targeting TGF-β1 Receptor for Anticancer Therapy

Objective The research goal focuses on discovering potential blocking substances for the Transforming Growth Factor-beta 1 (TGF-β1) signalling pathway that functions as a critical regulator during cancer progression. A research analysis uses natural compounds from Catharanthus roseus (Madagascar Periwinkle) and Tranilast as reference compounds to determine their effects on TGF-β1 signalling pathways for developing new cancer treatments. Methods The research conducted a computational analysis using 59 active Catharanthus roseus compounds, including Tranilast. The ligand preparation sequence began with three-step processing that started with generating 3D conformers, followed by protonation state optimization, before finishing with energy minimization. Scientists minimized the energy of the TGF-β1 receptor (PDB ID: 1PY5) through heteroatom removal followed by residue addition. The docking method began with high-throughput virtual screening, followed by standard and extra precision docking procedures. The MM/GBSA protocol calculated binding free energy values during the analysis. The ADMET properties and drug-likeness evaluation occurred through SwissADME and ProTox-III platform analysis. Results The TGF-β1 receptor established strong chemical binding capabilities with multiple compounds. The five primary compounds evaluated for research included Petunidin 3-O-glucoside, Isorhamnetin, Quercetin, Kaempferol, and Hirsutidin 3-glucoside. These compounds exhibited favourable binding free energies and pharmacokinetic profiles. ADMET analysis confirmed their drug-like properties with minimal predicted toxicity. Tranilast served as a benchmark for comparison, validating the efficacy of the natural compounds. Conclusion This study highlights the therapeutic potential of Catharanthus roseus-derived compounds as inhibitors of the TGF-β1 signalling pathway. Their strong binding affinities and favourable pharmacokinetic profiles suggest their viability as candidates for further preclinical evaluation in cancer therapy. The findings underscore the importance of exploring natural products as sources for targeted cancer treatments. Level of Evidence: Computational study; Level V.