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Prediction of Oral Drug Absorption in Rats from In Vitro Data
Prediction of Oral Drug Absorption in Rats from In Vitro Data
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Prediction of Oral Drug Absorption in Rats from In Vitro Data
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Prediction of Oral Drug Absorption in Rats from In Vitro Data
Prediction of Oral Drug Absorption in Rats from In Vitro Data

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Prediction of Oral Drug Absorption in Rats from In Vitro Data
Prediction of Oral Drug Absorption in Rats from In Vitro Data
Journal Article

Prediction of Oral Drug Absorption in Rats from In Vitro Data

2023
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Overview
PurposeIn drug discovery, rats are widely used for pharmacological and toxicological studies. We previously reported that a mechanism-based oral absorption model, the gastrointestinal unified theoretical framework (GUT framework), can appropriately predict the fraction of a dose absorbed (Fa) in humans and dogs. However, there are large species differences between humans and rats. The purpose of the present study was to evaluate the predictability of the GUT framework for rat Fa.MethodThe Fa values of 20 model drugs (a total of 39 Fa data) were predicted in a bottom-up manner. Based on the literature survey, the bile acid concentration (Cbile) and the intestinal fluid volume were set to 15 mM and 4 mL/kg, respectively, five and two times higher than in humans. LogP, pKa, molecular weight, intrinsic solubility, bile micelle partition coefficients, and Caco-2 permeability were used as input data.ResultsThe Fa values were appropriately predicted for highly soluble drugs (absolute average fold error (AAFE) = 1.65, 18 Fa data) and poorly soluble drugs (AAFE = 1.57, 21 Fa data). When the species difference in Cbile was ignored, Fa was over- and under-predicted for permeability and solubility limited cases, respectively. High Cbile in rats reduces the free fraction of drug molecules available for epithelial membrane permeation while increasing the solubility of poorly soluble drugs.ConclusionThe Fa values in rats were appropriately predicted by the GUT framework. This result would be of great help for a better understanding of species differences and model-informed preclinical formulation development.