Optimization of a liquid chromatography method for the analysis of related substances in daclatasvir tablets using design of experiments integrated with the steepest ascent method and Monte Carlo simulation

Abstract

Analytical method for the determination of related substances (RS) in Daclatasvir tablets was optimised using quality by design (QbD) approach. Seven degradants (each more than 1.0%) generated during oxidation study, adversely affected the selectivity of the method. Coelution of the degradant peaks with API and known impurities, suggested failure in developing a stability indicating method. To overcome the shortcomings and develop a robust method, QbD principles were incorporated. Resolution was the critical quality attribute (CQA) and buffer pH, column oven temperature, gradient slope and flow rate were the critical method variables (CMVs) studied through design of experiments (DoE). Discovery of an unknown impurity (named as impurity D, about1.0%) was a key finding from this DoE study. The most crucial responses viz. Resolution between impurity D and the main peak and resolution between the main peak and impurity E demanded contradictory pH requirements. To select the right pH, responses were prioritised and eventually to attain the desired resolution between Daclatasvir and impurity E the value for pH was fixed to 3.0. Next, to improve resolution between impurity D and Daclatasvir, method of steepest ascent was applied to locate an apt value for column oven temperature. Accordingly, experiments were performed at different temperatures along the path of rapid increase in response. Finally, at 45 °C (pH :3.0), both the critical pairs were well resolved. The global optimum was determined through a Response surface methodology (RSM) design with pH and column oven temperature as critical factors. pH 3.0, column oven temperature 44 °C, % MP. B 45% and flow rate 1.0 mL min−1 was found to be the optimum condition. Further, the design space was complimented by establishment of a robust zone through Monte Carlo simulation and capability analysis. An analytical control strategy (ACS) was set up to ensure that the method repeatedly meets its acceptance criteria. The optimised method was successfully validated within the factor ranges mentioned in the ACS. Despite various intricacies, the QbD approach facilitated systematic optimisation of a stability indicating robust method.