Prediction of human intestinal permeability using artificial membrane permeability

Area of Science:

• Pharmacokinetics and Drug Absorption
• Biophysical Chemistry
• Computational Chemistry

Background:

• Accurate prediction of human intestinal permeability (P_eff) is crucial for drug development.
• In vitro models are needed to estimate P_eff, reducing the need for animal and human studies.
• Existing models may not fully capture the complexities of drug transport across the intestinal barrier.

Purpose of the Study:

• To evaluate the correlation between human intestinal permeability (P_eff) and a corrected artificial membrane permeability model (P_PAMPA-PP-RF).
• To develop a predictive scheme for P_eff incorporating unstirred water layer effects.
• To assess the utility of P_PAMPA-PP-RF as an in vitro surrogate for P_eff.

Main Methods:

• Utilized 18 passively absorbed drug datasets for P_eff analysis.
• Employed a bio-mimetic artificial membrane permeability assay corrected by the paracellular pathway model based on the Renkin function (P_PAMPA-PP-RF).
• Incorporated the effect of the unstirred water layer into the prediction scheme.

Main Results:

• A high correlation coefficient (CC) of 0.91 was observed between predicted and observed logP_eff.
• Excluding specific compounds (furosemide, hydrochlorothiazide, creatinine) improved the CC to 0.97.
• The uncorrected model underestimated P_eff for small, cationic, and hydrophilic compounds, highlighting the importance of paracellular pathway correction.

Conclusions:

• The P_PAMPA-PP-RF model, corrected for the paracellular pathway and unstirred water layer, demonstrates strong predictive power for P_eff.
• This in vitro model serves as an adequate surrogate for estimating human intestinal permeability.
• The findings support the use of P_PAMPA-PP-RF in early-stage drug discovery to predict oral absorption.