Assessing the risk of pH-dependent absorption for new molecular entities: a novel in vitro dissolution test, physicochemical analysis, and risk assessment strategy.

Weak base therapeutic agents can show reduced absorption or large pharmacokinetic variability when coadministered with pH-modifying agents, or in achlorhydria disease states, due to reduced dissolution rate and/or solubility at high gastric pH. This is often referred to as pH-effect. The goal of this study was to understand why some drugs exhibit a stronger pH-effect than others. To study this, an API-sparing, two-stage, in vitro microdissolution test was developed to generate drug dissolution, supersaturation, and precipitation kinetic data under conditions that mimic the dynamic pH changes in the gastrointestinal tract. In vitro dissolution was assessed for a chemically diverse set of compounds under high pH and low pH, analogous to elevated and normal gastric pH conditions observed in pH-modifier cotreated and untreated subjects, respectively. Represented as a ratio between the conditions, the in vitro pH-effect correlated linearly with clinical pH-effect based on the Cmax ratio and in a non-linear relationship based on AUC ratio. Additionally, several in silico approaches that use the in vitro dissolution data were found to be reasonably predictive of the clinical pH-effect. To explore the hypothesis that physicochemical properties are predictors of clinical pH-effect, statistical correlation analyses were conducted using linear sequential feature selection and partial least-squares regression. Physicochemical parameters did not show statistically significant linear correlations to clinical pH-effect for this data set, which highlights the complexity and poorly understood nature of the interplay between parameters. Finally, a strategy is proposed for implementation early in clinical development, to systematically assess the risk of clinical pH-effect for new molecular entities that integrates physicochemical analysis and in vitro, in vivo and in silico methods.