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Analytical quality-by-design (AQbD) guided development of a robust HPLC method for the quantification of plumbagin from Plumbago species

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TAYLOR & FRANCIS INC
DOI: 10.1080/10826076.2021.1973027

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AQbD; Chitrak; design of experiment; Plumbago indica; Plumbago zeylanica

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The study developed a reverse-phase HPLC method for the quantification of plumbagin using an AQbD approach, optimizing critical method parameters through Box-Behnken Design experiment and validation following ICH guidelines. The developed method showed high precision and sensitivity, with the highest plumbagin content found in P. indica roots from different climatic conditions, suggesting the potential application of AQbD approach in optimizing plant metabolite extraction in other plant systems.
The present study was framed with the aim to develop a simple, efficient, and robust reverse-phase HPLC method for the quantification of plumbagin utilizing an analytical quality-by-design (AQbD) approach. HPLC was performed in Waters made high-performance liquid chromatography equipped with Waters 2996 photodiode array detector (detection at 254 nm), and reversed-phase Waters inertsil C-18 column. The AQbD approach helps in finding the most suitable critical analytical attributes (CAAs) as well as high-risk critical method parameters (CMPs). Using the Box-Behnken Design experiment, the selected CMPs were optimized for the higher recovery of plumbagin. Further, the developed method was validated following International Conference on Harmonization (ICH) Q2 (R1) guidelines. The low divergence in intra- and inter-day experiments, and low LOD (2.4 mu g/mL) and LOQ (7.2 mu g/mL) values justify the high precision and sensitivity of the developed method. The developed method was further tested for the quantification of plumbagin from the two different plant source collected from two different climatic conditions. Results show that the maximum plumbagin content was found in P. indica roots. In conclusion, the AQbD approach could be used in other plant systems for the optimization of different variables to obtain a higher amount of targeted plant metabolite.

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