Design of Experiment Mediated Development of Stability Indicating High Performance Thin Layer Chromatography Method Invoking Failure Mode Effect Analysis Based Risk Assessment in Estimation of Edoxaban Tosylate Monohydrate

Abstract:

Aim: Development of stability indicating high performance thin layer chromatographic method for quantification of edoxaban tosylate monohydrate in its pharmaceutical dosage form using quality by design approach. Methods: Degradation was performed in acidic, alkaline, neutral, oxidative and photolytic conditions. Analytical target profile was identified, based on which failure modes were identified by brainstorming session and preliminary trials. Each failure mode was assigned a risk priority number based on its severity and detectability. Identified potential method variables were further screened by Taguchi OA design. 3² full factorial design was applied for optimization of critical factors. The developed analytical method was validated using the Q2R1 guideline of the International Council for Harmonization. Results: In acidic, alkaline, and oxidative environments, the substance was shown to be degraded. After failure mode analysis, seven factors were found potential for estimation as it shows high risk priority number (RPN). Two important method variables, the volume of modifier and migration distance, were screened using screening design from these seven components. For the estimation of edoxaban tosylate monohydrate, the response surface model created design space and a control strategy was devised. Method found to be accurate, precise, and linear, with detection limit 1.021 ng/band and quantification limit 3.095 ng/band. Conclusion: Using methanol-ethyl acetate-triethylamine (6:4:0.2, v/v) as mobile phase on silica gel GF₂₅₄ plate, described method was effectively employed for estimation of edoxaban tosylate monohydrate in its tablet dosage form.

Key words: Edoxaban tosylate monohydrate, Stability indicating High Performance Thin Layer Chromatography, Failure mode effect analysis, 32 full factorial design, Design of experiment.