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

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.

Revealing the inner workings of the power function algorithm in Charged Aerosol Detection: A simple and effective approach to optimizing power function value for quantitative analysis.

In recent years, charged aerosol detection (CAD) has become a valuable tool for fast and efficient quantitative chromatographic analysis of drug substances with weak UV absorption. In analytical method development using CAD, the power function settings available in the instrument software are key for linearization of the signal response with respect to analyte concentration. However, the relatively poor understanding of the power function algorithm has limited a more widespread use of CAD for quantitative assays, especially in the late stage of method validation and GMP laboratories. Herein, we present an approach to understand the inner workings of the power function value (PFV), the PFV optimization algorithm, as well as a method to determine the optimum PFV based on the signals acquired at PFV = 1 (default CAD settings). The exponent and the constant in the PFV equation used for modeling follow a trend as a function of PFV. The CAD signal at any PFV was modeled based on the signal acquired at PFV = 1, the modelling was successful for two analytes at different concentration levels on two different CAD detectors of the same model. This method reveals the functionality of the PFV which substantially simplifies the workflow needed to optimize the detector signal. The accuracy between the experimental and theoretical results showed high correlation and always resulted in the same optimum PFV determined by both ways. The approach described in this investigation simplifies the selection of the optimum PFV at which the signal is more linear, the signal-to-noise is higher, and the area reproducibility is better. The power function algorithm elucidated herein enables determination of optimum PFV from minimal experimental output and excellent overall accuracy. This paper provides an approach that includes no data transformation outside the vendor software, a very important requirement to easily validate and report results in a GMP environment.