Multi-Target Pharmacological Mechanisms of Cannabidiol in Breast, Colorectal, and Lung Cancer: An Integrated Network Pharmacology and Molecular Docking Study
TL;DR
Combining pooled cancer-associated network pharmacology with co-crystallized ligand benchmarking suggests that CBD may exert its biological activity through coordinated modulation of multiple cancer-related signaling pathways rather than a single molecular target.
Abstract
Cannabidiol (CBD), the principal non-psychoactive phytocannabinoid of Cannabis sativa, exhibits diverse pharmacological activities through interactions with multiple molecular targets. Thus, breast, colorectal, and lung cancers arise from distinct molecular mechanisms. This study investigated the potential multi-target pharmacological mechanisms of CBD using an integrated approach combining network pharmacology and molecular docking. CBD-associated targets from three prediction platforms were intersected with disease-associated genes for each cancer type, yielding 143 overlapping targets that formed a significantly enriched protein–protein interaction network. Maximal Clique Centrality (MCC) analysis identified 10 hub proteins, including SRC, SIRT1, PTGS2 (COX-2), PPARG, NFKB1, MMP2, IGF1R, ESR2, ESR1, and EGFR, which represent key regulators of hormone signaling, inflammation, cell proliferation, and tumor progression. Molecular docking against these targets, benchmarked using each protein’s authentic co-crystallized ligand, predicted predominantly moderate binding affinities for CBD. Compared with the corresponding reference ligands, CBD generally exhibited lower predicted binding affinity, although comparable or slightly stronger scores were observed for PTGS2, ESR2, and EGFR. Independent validation using AutoDock Vina demonstrated overall agreement with the MOE docking results, supporting the robustness of the predicted binding profiles. Collectively, these findings suggest that CBD may exert its biological activity through coordinated modulation of multiple cancer-related signaling pathways rather than a single molecular target. By integrating pooled cancer-associated network pharmacology with co-crystallized ligand benchmarking, this study provides a computational framework for prioritizing biologically relevant CBD targets for future experimental validation. These findings should be regarded as hypothesis-generating rather than evidence of clinical efficacy.