(W1130-10-64) Refining Predictions of Food Effects on Drug Absorption: Models Predicting Gastric Emptying and Bile Concentrations Based on Fat Content and Caloric Intake

Purpose: This study presents the development of mechanistic fed-state models that improve the understanding of food effects on drug absorption. By incorporating the influence of meal calories and fat content on gastric emptying and intestinal bile concentrations, these models offer enhanced accuracy compared to current approaches. They provide valuable insights into clinical data, particularly for BCS class II molecules, where the impact of fed/fasted state varies depending on the meal composition. This has implications for drug product labeling, cost reduction in food effect studies, and optimization of formulation development timelines. This work represents a significant step towards refining the mechanistic description of meal-drug interactions within physiologically-based pharmacokinetic (PBPK) software.

Methods: Data from 15 literature studies on gastric emptying rates with various meal types and caloric intake were analyzed. The meals encompassed a wide range of sources, including glucose and/or glucose/pectin solutions, ensure drinks, emulsified oils, and solid foods such as breakfast sandwiches, pancakes, hamburgers, salads, and cereals. Correlations were established to predict zero-order gastric emptying based on meal calories, integrated into Gastroplus v9.8 software (Simulations Plus, Inc.). Additionally, insights from literature on the impact of a high-fat diet on bile production, excretion, and colonic bile concentrations were used to construct a secretion and reabsorption model. This model predicted steady-state bile concentrations in the intestinal lumen following meals with varying fat content. These predicted bile concentrations were incorporated into the GastroPlus v9.8 model to assess changes induced by different meal types. The developed models were validated using fed and fasted state pharmacokinetic data from eight compounds, including literature sources and proprietary consulting studies, to evaluate their performance in predicting Tmax and Cmax based on the new predictions of bile salt concentrations and gastric emptying.

Results: The incorporation of meal calories and fat content in the calculation of zero-order gastric emptying and bile concentrations yielded improved utility for multiple compounds, including Dolutegravir, Axitinib, and the four internal proprietary datasets. Compared to the default Gastroplus model, the new model exhibited reduced errors in predicting Tmax, which had historically been underpredicted due to the fixed 1-hour gastric emptying time. The datasets demonstrated that longer zero-order gastric emptying times were 75% more predictive of Tmax compared to the default Gastroplus fed state model. For example, for Dolutegravir, the improved fed state model based on calories and fat reduced the average predicted Tmax error for different meal types to 6.6% from the default Gastroplus model's 20.3% error. Similarly, for Axitinib, the new model accurately predicted a Tmax of 3.2 hours for a high-fat meal, compared to the default Gastroplus model's prediction of 1.92 hours.

Conclusion: This study presents advances in predicting food effects on drug absorption by enhancing mechanistic models of gastric emptying and bile concentrations. While the findings demonstrate promising utility, further research is warranted to investigate additional factors such as the influence of acids or proteins on gastric pH. Moreover, future work should focus on developing comprehensive models that capture complex drug-food interactions, including partitioning into oils and proteins, to enable accurate predictions of intricate food effects such as drug sequestration or enhancement of dissolution.