This study quantitatively explores the mechanisms underpinning the effects of model pharmaceutical polymeric precipitation inhibitors (PPIs) on the crystal growth and, in turn, maintenance of supersaturation of indomethacin, a model poorly water-soluble drug. A recently developed second-derivative UV spectroscopy method and a first-order empirical crystal growth model were used to determine indomethacin crystal growth rates in the presence of model PPIs. All three model PPIs including HP-β-CD, PVP, and HPMC inhibited indomethacin crystal growth at both high and low degrees of supersaturation (S). The bulk viscosity changes in the presence of model PPIs could not explain their crystal growth inhibitory effects. At 0.05% w/w, PVP (133-fold) and HPMC (28-fold) were better crystal growth inhibitors than HP-β-CD at high S. The inhibitory effect of HP-β-CD on the bulk diffusion-controlled indomethacin crystal growth at high S was successfully modeled using reactive diffusion layer theory, which assumes reversible complexation in the diffusion layer. Although HP-β-CD only modestly inhibited indomethacin crystal growth at either high S (∼15%) or low S (∼2-fold), the crystal growth inhibitory effects of PVP and HPMC were more dramatic, particularly at high S (0.05% w/w). The superior crystal growth inhibitory effects of PVP and HPMC as compared with HP-β-CD at high S were attributed to a change in the indomethacin crystal growth rate-limiting step from bulk diffusion to surface integration. Indomethacin crystal growth inhibitory effects of all three model PPIs at low S were attributed to retardation of the rate of surface integration of indomethacin, a phenomenon that may reflect the adsorption of PPIs onto the growing crystal surface. The quantitative approaches outlined in this study should be useful in future studies to develop tools to predict supersaturation maintenance effects of PPIs.
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.
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