Comprehensive Greenness Assessment of an RP-HPLC Method for Simultaneous Estimation of Quercetin and Daidzein in Cubosomes: A QbD and Lean Six Sigma Approach
摘要
Quercetin (QUC) and Daidzein (DAI), potent bioactive flavonoids with promising therapeutic potential, are routinely encapsulated within lipid-based nanovesicles to address bioavailability constrains. Their simultaneous quantification in complex matrices requires a sensitive, robust, and sustainable analytical method. The study introduces a green, QbD-guided reverse-phase high-performance liquid chromatography (RP-HPLC) method for simultaneous estimation, incorporating lean six sigma to ensure precision, efficiency, and eco-friendliness.
MethodsA comprehensive quality by design (QbD) approach was implemented, initiating with risk identification via an Ishikawa diagram and risk priority number (RPN) analysis. Critical method parameters were screened using a Taguchi 27 Orthogonal array design, followed by optimization through a Box–Behnken design (BBD). Chromatographic separation was executed using a Phenomenex Luna F.
ResultsThe optimized method exhibited superior chromatographic attributes including peak symmetry, baseline resolution (>2.0) and shorter retention times of 7.06min for QUC and 6.15 for DAI ensuring rapid analysis within 9 min.. It demonstrated exceptional linearity (R2 > 0.999), precision (%RSD < 2%), and accuracy in accordance with International Council for Harmonisation ICH guidelines. Forced degradation assays affirmed its stability-indicating capacity. Greenness metrics were thoroughly evaluated via various tools, confirming minimal solvent consumption and reduced environmental impact.
ConclusionThis study demonstrates a rapid, precise, and sustainability-oriented RP-HPLC method for the concurrent estimation of QUR and DAI in a cubosomal matrices. Its methodological rigor, statistical robustness, and alignment with eco-analytical benchmarks render it a highly viable tool for routine quality control and regulatory-compliant phytopharmaceutical research.
Graphical Abstract