Network toxicology-aided identification of potential toxicological mechanisms of rhodamine B in ovarian cancer: Insights from transcriptomics, molecular docking, molecular dynamics simulations, and machine learning modeling

Ovarian cancer is the most lethal gynecological malignancy, accounting for disproportionate cancer-related mortality due to late-stage diagnosis and limited therapeutic options. Despite documented cytotoxicity, genotoxicity, hepatotoxicity, and reproductive toxicity of Rhodamine B, its toxic molecular interaction landscape in ovarian cancer remains uncharacterized. Therefore, this study employed an integrative network toxicology approach, along with machine learning, molecular docking, and molecular dynamics (MD) simulations, to systematically map Rhodamine B's interactions against ovarian cancer-associated targets. Multi-database target prediction cross-referenced against curated ovarian cancer genes and differentially expressed genes from GEO datasets GSE4122 and GSE203024 of ovarian cancer identified 26 convergent targets from 331 unique predictions. Network topology (degree, betweenness, closeness, MCC, and MNC) analysis identified BCL2, BCL2L1, MMP2, and MMP9 as core targets spanning apoptosis regulation and matrix remodeling pathways. Molecular docking revealed energetically favorable binding affinities ranging from −6.4 kcal/mol (BCL2L1) to −7.8 kcal/mol (MMP2). Furthermore, MD simulations suggested stable structural behavior of the BCL2, BCL2L1, and MMP9 complexes throughout the 100 ns simulation (RMSD <3.0 Å). Post-dynamics MMPBSA calculations identified BCL2, MMP9, and BCL2L1 as the most thermodynamically stable interaction partners (ΔG = −32.593 ± 4.721, −23.439 ± 4.742, and − 21.909 ± 5.791 kcal/mol). Finally, the top machine learning models predicted the pIC 50 value of Rhodamine B to be 4.520, denoting Rhodamine B as inactive (active threshold: 5.0) against the Caov-3 ovarian cancer cell line. These findings suggest Rhodamine B as a multi-target toxic agent, potentially disrupting apoptotic and invasive signaling associated with ovarian cancer, consistent with machine learning predictions indicating inactivity against the Caov-3 ovarian cancer cell line. Overall, these findings provide a foundation for further experimental studies to validate the computationally predicted toxic interaction profiles of Rhodamine B through cell viability, oxidative stress, apoptosis, and genotoxicity assays in ovarian cancer cell lines, as well as in vivo animal models.