Charged aerosol detection in early and late-stage pharmaceutical development: selection of regressionmodels at...

In recent years, the use of quantitative liquid chromatography (LC) coupled charged aerosol detection (CAD) for poor UV absorbing analytes in multicomponent mixtures has grown exponentially across academic and industrial sectors. The ballpark of previous LC-CAD reports is focused on practical applications, as well as optimization of critical parameters such as: response dependencies on temperature, nebulization process, analyte volatility, and mobile-phase composition. However, straightforward approaches to deal with the characteristic nonlinear response of CAD still scarce. A highly overlooked parameter is the power function value (PFV), whose optimization enables a detection signal that is more linear with higher signal-to-noise ratio (S/N) and lower relative standard deviation (RSD) of area counts. Herein, a systematic investigation of different regression models (log-log, first-and second-degree polynomial) by both interpolation and extrapolation process in conjunction with PFV optimization throughout the development of LC-CAD assays is reported. The accuracy of the results via interpolation is always good (< 5%) when operating in the vicinity of the optimum PFV regardless the regression model choice. On the contrary, extrapolation process only worked when applying log-log regression at the optimum PFV (accuracy <5%). This outcome indicates that a first-order regression via interpolation can be a safe and simple choice for quantitative LC-CAD in highly regulated laboratories (GLP, GMP, etc.). Whereas a straightforward extrapolation combined with log-log regression can enable the deployment of high-throughput LC-CAD assays, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization (medicinal chemistry, discovery, biocatalysis, process chemistry, etc.). This approach is crucial in developing quantitative LC-CAD assays for poor UV absorbing pharmaceuticals that are sensitive, precise, accurate and robust across early and late-stage pharmaceutical development.