Implementation of analytical quality‐by‐design and green analytical chemistry approaches for the development of robust and ecofriendly UHPLC analytical method for quantification of chrysin

Abstract

Amalgamation of Quality by Design approach with the concepts of green analytical chemistry was employed for holistic understanding of the method, along with minimal environmental adverse impacts. Thus, the current analytical method was precisely evaluated employing risk assessment studies followed by factor screening studies in order to select the critical method parameters. Optimization for the chosen critical method parameters was carried out employing a 3‐factor‐3‐level‐17‐run Box Behnken Design. Analytical design region marking the optimal method performance was identified using numerical and graphical optimization. Optimal method conditions include use of a C8 column, along with acetonitrile and water in 80:20 v/v as mobile phase flowing at the rate of 0.5 mL/min with an injection volume of 5 µL at λmax of 267 nm. Validation studies established the high efficiency, robustness, and sensitivity of the developed method for quantification of chrysin in working standards as well as in biological matrix, that is, plasma. Optimal method exhibited high sensitivity with 0.028 µg/mL as LOD and 0.075 µg/mL as LOQ. Percent recovery during accuracy testing was found to be between 98.34 and 104.80%, thus, demonstrating method to be highly accurate. Thus, this method finds its utility in qualitative and quantitative analysis of chrysin. The obtained score for the current developed method was found to be 81 on the basis of penalty points of eco scale, indicating it to be a greener analytical method.

Quality by design‐steered development and validation of analytical and bioanalytical methods for raloxifene: Application of Monte Carlo simulations and variance inflation factor

“…Linear calibration plot was validated for a concentration range of 0.10–100 μg mL −1 , by injecting a specified volume (10 μL) of each RLX solution. A linear correlation plot between the magnitudes of the observed and predicted responses was constructed using least squares regression analysis, along with the magnitudes of R , using MS Excel (Microsoft Inc., Washington, USA; Sharma et al, 2020).…”