The goal of this work was to develop an in silico model that allows predicting segmental-dependent permeability throughout the small intestine (SI). In vivo permeability of 11 model drugs in 3 SI segments (jejunum, mid-SI, ileum) was studied in rats, creating a data set that reflects the conditions throughout the SI. Then, a predictive model was developed, combining physicochemical drug properties influencing the underlying mechanism of passive permeability: Log p, polar surface area, MW, H-bond count, and Log fu, with microenvironmental SI conditions. Excellent correlation was evident between the predicted and experimental data (R2 = 0.914), with similar predictability in each SI segment. Log p and Log fu were identified as the major determinants of permeability, with similar contribution. Total H-bond count was also a significant determinant, followed by polar surface area and MW. Leaving out any of the model parameters decreased its predictability. The model was validated against 5 external drugs, with excellent predictability. Notably, the model was able to predict the segmental-dependent permeability of all drugs showing this trend experimentally. Model predictability was better in the high-permeability versus low-permeability range. Overall, our approach of constructing a straightforward in silico model allowed reliable predictions of segmental-dependent intestinal permeability, providing new insights into relative effects of drug-related factors and gastrointestinal environment on permeability.
Keywords: BCS; in silico predictions; intestinal permeability; oral drug absorption; segmental-dependent permeability; small intestine.
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