Pharmaceutical research · 2026
A Population-based Gastrointestinal Transit Model Using Individual-Level Imaging Data.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Xie Feifan, Xie Yuxin, Xie Yehua, Cheng Zeneng, Liu Feiyan
Аннотация
PURPOSE: Gastrointestinal (GI) transit is a key determinant of oral drug absorption. Existing GI transit models derived from summary transit-time distributions provide limited characterization of inter-individual variability. This study aimed to develop a population-based GI transit model using individual-level imaging data.
METHODS: Individual gastric emptying (GE), small intestinal (SI) transit, and colon arrival (CA) data from imaging studies of non-absorbed markers in healthy adults were extracted from the literature. A population-based GI transit model was developed using sequential Weibull GE and transit compartment models. Formulation type and prandial state were evaluated as covariates, and inter-individual variability was estimated on key transit parameters.
RESULTS: Individual data comprising 90 GE, 26 SI, and 39 CA profiles were included. The final model consisted of a Weibull GE component and a six-compartment SI transit model. Formulation type and prandial state significantly influenced GE. Mean GE time was 0.16 h for liquid formulations under fasted conditions, while mean SI transit time was 2.97 h with substantial inter-individual variability (CV = 50.0%).
CONCLUSION: This population-based GI transit model, informed by individual imaging data, quantitatively captures both mean transit behavior and inter-individual variability and may facilitate mechanistic absorption frameworks for oral drug exposure prediction.
METHODS: Individual gastric emptying (GE), small intestinal (SI) transit, and colon arrival (CA) data from imaging studies of non-absorbed markers in healthy adults were extracted from the literature. A population-based GI transit model was developed using sequential Weibull GE and transit compartment models. Formulation type and prandial state were evaluated as covariates, and inter-individual variability was estimated on key transit parameters.
RESULTS: Individual data comprising 90 GE, 26 SI, and 39 CA profiles were included. The final model consisted of a Weibull GE component and a six-compartment SI transit model. Formulation type and prandial state significantly influenced GE. Mean GE time was 0.16 h for liquid formulations under fasted conditions, while mean SI transit time was 2.97 h with substantial inter-individual variability (CV = 50.0%).
CONCLUSION: This population-based GI transit model, informed by individual imaging data, quantitatively captures both mean transit behavior and inter-individual variability and may facilitate mechanistic absorption frameworks for oral drug exposure prediction.