Importance of Gastric Secretion and the Rapid Gastric Emptying of Ingested Water along the Lesser Curvature (Magenstrasse) in Predicting the In Vivo Performance of Liquid Oral Dosage Forms in the Fed State Using a Modeling and Simulation

The objective of the present study was to develop an in silico model of the stomach for predicting oral drug absorption in fed humans. We focused on a model capable of simulating dynamic fluid volume changes and included a simulated Magenstrasse stomach road, a route along the lesser curvature that often carries fluids rapidly to assess the gastric emptying of drugs. Two types of model liquid drug formulations, liquid-filled soft gelatin capsules (enzalutamide, cyclosporine, and nifedipine) and oral solutions (levofloxacin and fenfluramine), were used. An in silico model was assembled, and simulations were performed using Stella Professional software. The secretion rate of the gastric juice induced by food ingestion was assessed along with the gastric emptying of the ingested water via the Magenstrasse in the fed state. The model for the fed state successfully described the in vivo performance of the model drug formulations. These results clearly indicate the importance of including gastric secretion and the kinetics of Magenstra ss e when predicting the in vivo performance of dosage forms using an in silico modeling and simulation of fed humans. This simulation model should be further optimized to allow for the different physiological mechanisms following the ingestion of different types of meals, as well as modifications for interindividual and intraindividual variabilities in gastrointestinal physiology in the fed state in the future.

Automated summary

The objective of this study was to develop an in silico model for predicting oral drug absorption in fed humans. The model successfully described the in vivo performance of different liquid drug formulations and highlighted the importance of considering gastric secretion and the kinetics of gastric emptying in predicting drug performance.