Experimental design approach, screening and optimization of system variables, analytical method development of flurbiprofen in nanoparticles and stability-indicating methods for high-pressure liquid chromatography

Background: The development of chromatographic method and the validation of a sensitive, simple, efficient, and reversed-phase high-performance liquid chromatography (RP-HPLC) approach were adopted for the drug flurbiprofen (FBP) in nanoparticles formulation by using a design of experiment (DoE). The critical method variables (CMVs) were screened using a statistical two-level fractional factorial design (FFD) followed by optimization of the selected CMVs that influence the analytical responses (ARs) of the RP-HPLC process by using two-level full factorial design. Results: Statistical models are used to investigate the effects of system factors including column temperature, flow rate, and methanol in orthophosphoric acid (OPA) on the dependent responses, retention time, peak area, tailing factor, and theoretical plates in HPLC. The ideal column temperature (25 degrees C), flow rate (1 ml/min), and mobile phase (methanol 85 percent v/v in 0.05 percent OPA in water) were selected independently from the response surface at three levels (1, + 1, and 0) for further validation at constant solvent p(H) 2.75. Optimized method in the RP-HPLC resulted a retention time of 4.75 min, a peak area of 3975.12, a tailing factor of 0.73, and a total of 9697.7 theoretical plates followed by validation in accordance with the current ICH recommendations Q2 (R1). Linearity, precision, accuracy, assay, limit of detection (LOD), limit of quantification (LOQ), and robustness were all included in validation. The calibration curve was linear (r(2) = 0.9997, slope = 70.72) for the concentration of 10 to 50 mu g/ml, with a limit of detection of 0.14 mu g/ml. Furthermore, stability-indicating methods demonstrate that drug degradation is highest in the presence of basic circumstances (about 96.49%), followed by oxidation (about 76.41%), and acidic conditions (about 48.12%), whereas drug is stable in some extent under neutral, photo (sunlight), and dry heat conditions. Conclusions: Effect of independent variables on dependent responses was screened and optimized by using statistical software design. A method for drug development could be successfully implemented for the estimation of drug in nanoparticles formulation as well as for the routine analysis in bulk and pharmaceutical formulations. The high recovery and low relative standard deviation support the suitability of proposed method that could be employed.