Integrated in silico pharmacology of Pleurotus ostreatus-derived bioactive compounds targeting EGFR using network...

Cancer remains a major global health challenge, demanding the development of novel and mechanism-based therapeutic strategies. In the present study, an integrated computational approach involving network pharmacology, molecular docking, ADMET profiling, and molecular dynamics simulations was employed to investigate the anticancer potential of bioactive compounds derived from the mushroom Pleurotus ostreatus. Nine compounds with favourable predicted pharmacokinetic and toxicological properties were identified, yielding 138 potential cancer-related targets. Protein-protein interactions (PPIs) network analysis revealed 15 key hub genes, including CASP3, EGFR, ESR1, HSP90AA1, PPARG, MDM2, PARP1, SRC, PIK3CA, RELA, JAK2, PTGS2, GSK3B, PIK3R1, and TLR4, which are associated with several cancer types such as breast, colorectal, liver, and lung cancers. Functional enrichment analysis indicated the involvement of these targets in multiple cancer-related signaling pathways. Among these hub genes, EGFR emerged as a central therapeutic target due to its high network connectivity and involvement in multiple cancer-associated signaling pathways related to proliferation, survival, metastasis, and therapeutic resistance. Its prominent position within the interaction network further supports its potential as a key target for anticancer intervention. Molecular docking analysis demonstrated that lovastatin showed a strong predicted binding affinity toward EGFR, suggesting possible inhibitory potential. Molecular dynamics simulations further supported the stability of the lovastatin-EGFR complex. In addition, ADMET analysis indicated favourable drug-likeness and safety-related properties for the selected compounds. Overall, this study provides a systems-level computational investigation of Pleurotus ostreatus-derived compounds and their potential interactions with cancer-associated targets. However, these findings are based on computational predictions and require further experimental validation to confirm their biological and therapeutic relevance.

The online version contains supplementary material available at 10.1007/s40203-026-00690-9.