Leveraging prior knowledge for improved retention prediction in reversed-phase HPLC

Abstract

Prior information about analyte retention is often implicitly incorporated into the method development workflow. This prior knowledge can stem from various sources, such as the analyte's structure, analyte's properties, existing literature, or the analyst's experience. Alternatively, prior information can be formally integrated into the method development workflow using Bayesian reasoning. In such cases, it can be represented through the model structure, covariate relationships (e.g. quantitative-structure retention relationships), and population-level parameters derived from multilevel models or other sources. Population-level parameters are the same for each analyte belonging to a certain set of analytes and as such help predict the individual-level (analyte-specific) parameters given any type of preliminary data. The use of prior information and multilevel modeling framework enables development of an experimental design that leads to the desired precision of chromatographic predictions across a wide range of conditions and for a diverse set of analytes. This approach offers greater accuracy compared to optimizing conditions for a single or typical analyte. In this study maximization of the Bayesian D-optimality criterion was employed to identify an optimal set of experiments for diverse set of analytes (acids, bases with a wide range of lipophilicity). The benefit of incorporating prior information was emphasized, and simulations based on a recently developed mechanistic model validated the benefits of combining optimal design theory, multilevel models, and prior information to obtain more efficient experimental designs in Reversed-Phase HPLC.