Predictive models for drugs exhibiting negative food effects based on their biopharmaceutical characteristics

Abstract

Context: A drug is defined to exhibit food effects if its pharmacokinetic parameter, area under the curve (AUC_0–∞) is different when co-administered with food in comparison with its administration on a fasted stomach. Food effects of drugs administered in immediate release dosage forms were classified as positive, negative, and no food effects.

Objective: In this study, predictive models for negative food effects of drugs that are stable in the gastrointestinal tract and do not complex with Ca²⁺ are reported.

Methods: An empirical model was developed using five drugs exhibiting negative food effects and seven drugs exhibiting no food effects by multiple regression analysis, based on biopharmaceutical properties generated from in vitro experiments. An oral absorption model was adopted for simulating negative food effects of model compounds using in situ rat intestinal permeability.

Results: Analysis of selected model drugs indicated that percent food effects correlated to their dissociation constant, K (K_a or K_b) and Caco-2 permeabilities. The obtained predictive equation was: Food effect (%) = (2.60 × 10⁵·P_app) − (2.91 × 10⁵·K) − 8.50. Applying the oral absorption model, the predicted food effects matched the trends of published negative food effects when the two experimental pH conditions of fed and fasted state intestinal environment were used.

Conclusion: A predictive model for negative food effects based on the correlation of food effects with dissociation constant and Caco-2 permeability was established and simulations of food effects using rat intestinal permeability supported the drugs’ published negative food effects. Thus, an empirical and a mechanistic model as potential tools for predicting negative food effects are reported.

Keywords::

• Predictive model
• biopharmaceutical properties
• negative food effects
• Caco-2 permeability
• dissociation constant