Promising synergistic interactions and mixture optimization of safranal, crocin, and crocetin from Moroccan Crocus sativus L. with enhanced antimicrobial activity - Baraich, Abdellah; Sadougui, Ibrahim; Elbouzidi, Amine; Haddou, Mounir; Taibi, Mohamed; Beraich, Abdessamad; El Moussaoui, Fatima Zahrae; Almarfadi, Omer M.; Mothana, Ramzi A.; Hawwal, Mohammed F.; Asehraou, Abdeslam; Addi, Mohamed; et al. (Frontiers in Pharmacology, 2026)
This study explores the antimicrobial activity of three principal bioactive constituents of Crocus sativus L.—safranal, crocin, and crocetin—using a simplex-centroid mixture design to evaluate their individual and synergistic effects. Twelve formulations were tested against Staphylococcus aureus, Escherichia coli, Candida albicans, and Geotrichum candidum. Minimum inhibitory concentrations (MICs) were determined via microdilution assays, and the results were fitted using special cubic regression models. The models exhibited strong predictive accuracy (R2 = 0.97–0.99). Safranal showed the highest individual activity against S. aureus (σ1 = 169.77, p = 0.0053), while crocin exerted the strongest effect on E. coli (σ2 = 166.63, p < 0.0001). Significant synergistic interactions were observed between crocin and crocetin for both bacterial strains (σ23 = −290.34 for S. aureus; −170.28 for E. coli). Ternary mixtures displayed superior efficacy compared to single compounds, producing the lowest MIC values across all pathogens. The optimal antibacterial formulation—33.31% safranal, 33.29% crocin, and 33.39% crocetin—yielded MICs of 41.14% (v/v) (predicted) and 39.5% ± 0.75% (v/v) (experimental) against E. coli. Against S. aureus, a blend of 27% safranal, 33% crocin, and 38% crocetin resulted in a predicted MIC of 42.15% (v/v), confirmed experimentally at 38.13% ± 1.33% (v/v). For G. candidum, the optimized mixture reached MIC values of 9.13% (v/v) (predicted) and 10.24% ± 2.05% (v/v) (observed). These findings demonstrate that synergistic combinations of saffron-derived metabolites markedly enhance antimicrobial potency. Moreover, mixture design modeling emerges as an effective predictive and optimization strategy for developing reproducible, plant-based antimicrobial formulations targeting antibiotic resistance.