Predictive Analytical Quality by Design‐based Reversed‐Phase Ultra‐High‐Performance Liquid Chromatography Method for Olaparib Impurity Profiling Using Mechanistic Retention Modeling and Monte Carlo Risk Assessment

ABSTRACT

Olaparib, a poly(ADP‐ribose) polymerase inhibitor used in BRCA‐mutated cancers, requires stringent impurity control due to chronic high‐dose administration (up to 600 mg/day). This study describes a predictive reversed‐phase ultra‐high‐performance liquid chromatography (UHPLC) method for olaparib impurity profiling using an analytical quality by design framework integrating mechanistic retention modeling with statistical risk assessment. Critical method parameters, including mobile‐phase pH, column temperature, and gradient time, were investigated using a full‐factorial experimental design. The quadratic retention model demonstrated excellent predictive performance, with external validation showing a mean absolute prediction error of 1.38% (95% confidence interval: 0.81%–1.70%). Monte Carlo simulations ( n = 10 000) quantitatively assessed method robustness and established a statistically defined, method‐operable design region. The optimized method achieved baseline separation of olaparib and six related impurities within 15 min (minimum resolution ≥7.3, tailing factors ≤1.04). Validation followed the International Council for Harmonization (ICH) Q2(R1) guidelines, confirming specificity, linearity ( R ² > 0.999), accuracy (80%–120% recoveries), and precision, with a limit of quantification of 0.05% (meeting the ICH Q3B reporting threshold for the 600 mg/day dose). The method demonstrated a favorable environmental profile (Analytical GREEnness score = 0.74, no red categories in Green Analytical Procedure Index assessment), reflecting reduced solvent consumption (∼4.5 mL per run) through UHPLC miniaturization. This integrated approach provides a scientifically rigorous and sustainable framework for impurity profiling and lifecycle management in pharmaceutical analysis.