DoE-assisted development and validation of a stability-indicating HPLC-DAD method for simultaneous determination of five cannabinoids in Cannabis sativa L. based on analytical quality by design (AQbD) concept.

Introduction: Medical uses of Cannabis sativa L. have gained interest in recent decades, which highlights the need for defining appropriate quality specifications for Cannabis-based products. However, the complexity of plant matrices and structural similarity between cannabinoids make analytical development a challenging task. Thus, the application of analytical quality by design (AQbD)-driven approaches can favour the development of fit-for-purpose methods.

Objectives: To develop a high-performance liquid chromatography diode array detector (HPLC-DAD) method for simultaneous quantification of cannabidiol, Δ -tetrahydrocannabinol, cannabidiolic acid, tetrahydrocannabinolic acid, and cannabinol in C. sativa by applying an AQbD-driven approach.

Materials And Methods: Critical method attributes (CMA) were established following the analytical target profile. Critical method variables (CMV) were categorised based on risk assessment and literature review. Selected CMV regarding sample preparation and chromatographic conditions were optimised using response surface methodology (RSM). The working point was estimated by multiple response optimisation using Deringer's desirability function. The validity of the optimal conditions was confirmed experimentally. Method validation was performed according to ANVISA and ICH guidelines. Relative response factors (RRFs) were also determined.

Results And Discussion: Baseline resolution of 12 major cannabinoids was achieved in a 35 min chromatographic analysis. All experimental responses obtained during confirmatory analyses were within the prediction intervals (PI ). Method's selectivity, linearity (10-100 μg/mL), precision, bias, extraction recovery, and ruggedness were satisfactorily demonstrated.

Conclusions: The application of an AQbD-driven approach allowed for a better understanding of the effects of the ensemble of CMV on the analyte's behaviour, enabling the definition of appropriate conditions to ensure consistent achievement of the intended method's performance.