A series of previously synthesized carbazole-based urea and thiourea derivatives (5a–5i) was subjected to an integrated computational investigation of their predicted interactions with epidermal growth factor receptor (EGFR) (PDB ID: 1M17) and their pharmacokinetic and dynamic behavior. Molecular docking yielded scores of −9.0 to −13.5 kcal/mol, with 5f showing the most favorable value (−13.5 kcal/mol). Redocking of the co-crystallized ligand AQ4 yielded an RMSD of 1.65 Å, supporting the docking protocol. SwissADME analysis revealed high lipophilicity, low predicted gastrointestinal absorption, and poor aqueous solubility across the series. Compound 5f showed a Consensus LogP of 7.05, two Lipinski violations, a bioavailability score of 0.17, and predicted P-glycoprotein substrate behavior. SwissTargetPrediction identified EGFR among the predicted targets of 5f, providing complementary computational rationale for its investigation. Three independent 100 ns molecular dynamics simulations of the EGFR–5f complex showed limited protein backbone deviations (mean RMSD: 0.217–0.282 nm), maintained overall compactness (mean Rg: 2.005–2.040 nm), and continued protein–ligand association despite replicate-dependent conformational sampling. Overall, 5f was computationally prioritized based on its favorable predicted EGFR interactions and sustained association over the investigated simulation timescale, while its physicochemical and pharmacokinetic limitations indicate the need for further structural optimization and experimental validation.

The chemical and quality characteristics of honey vary considerably according to floral source, geographical provenance, environmental conditions, and post-harvest handling, complicating its characterization and authentication. In this study, physicochemical properties, bioactive constituents, enzymatic activity, and exploratory chemometric patterns were evaluated in thirteen honey samples representing different declared botanical types, including sage, meadow, chestnut, acacia, and pine honeys, from different geographical origins. Physicochemical characterization comprised moisture content, refractive index, specific optical rotation, electrical conductivity, pH, free acidity, hydroxymethylfurfural (HMF), reducing sugars, ash content, qualitative mineral composition, and preliminary authenticity-related tests. Total phenolic content determined by the Folin–Ciocalteu assay (TPC-FC), total flavonoid content (TFC), total phenolic content determined by the Prussian Blue assay (TPC-PB), proline content, and diastase activity were also evaluated. Statistical analysis included one-way analysis of variance (ANOVA) followed by Tukey’s HSD test, Pearson correlation analysis, and principal component analysis (PCA). One-way ANOVA followed by Tukey’s HSD test indicated significant differences in the measured analytical responses among the individual honey samples for the evaluated bioactive parameters (p < 0.05); these comparisons were restricted to the investigated samples and were not interpreted as inferential differences among botanical or geographical populations. Pearson correlation analysis revealed several moderate-to-strong associations among the analytical variables; however, none remained statistically significant after Benjamini–Hochberg false discovery rate (FDR) correction for the 91 pairwise comparisons. Given the small number of independent honey samples (n = 13), these correlation patterns were interpreted as exploratory. PCA provided an exploratory representation of the multivariate structure of the dataset, with PC1, PC2, and PC3 explaining 22.51%, 20.88%, and 13.01% of the total variance, respectively, for a cumulative contribution of 56.40%. The principal components reflected contributions from multiple physicochemical, bioactive, and enzymatic variables; however, no clear or consistent grouping according to declared botanical type or geographical origin was observed. Taken together, the results demonstrate substantial compositional heterogeneity among the investigated honey samples and highlight the value of integrating physicochemical, bioactive, enzymatic, and exploratory chemometric information for multidimensional honey characterization. Given the small and unbalanced sample set and the absence of formal melissopalynological authentication, the observed multivariate patterns should be interpreted as exploratory rather than as evidence of definitive botanical or geographical classification.