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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
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In vitro experiments are crucial for understanding the transport and absorption of drugs through biological materials. These studies employ varied methods such as the diffusion cell method, the everted sac technique, and the everted ring technique.
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Carrier-mediated transport is a pivotal process in drug absorption, particularly for lipid-insoluble drugs, and encompasses facilitated diffusion and active transport. Facilitated diffusion allows drugs to move along their concentration gradient without energy expenditure, while active transport utilizes ATP to drive drug movement against this gradient.
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The pharmacokinetic journey of drugs from solid oral dosage forms into systemic circulation is multifaceted. It begins with disintegration, a prerequisite ensuring a solid dosage form's subdivision into minute particles. Dissolution occurs next as these granulated entities solubilize in gastrointestinal fluids. This solubilization is crucial for the succeeding stage, permeation, which describes the traversal of the drug across the intestinal membrane and its subsequent entry into the blood...
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Updated: Sep 19, 2025

Ex Vivo Intestinal Sacs to Assess Mucosal Permeability in Models of Gastrointestinal Disease
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In Silico Prediction of Human Intestinal Permeability (Caco-2) using QSPR Modelling for Efficient Drug Discovery.

Aayush Chowdhury1, Sayantani Garai2, Dipro Mukherjee2

  • 1Department of Computer Science and Engineering, University of Engineering & Management, Kolkata 700160, West Bengal, India.

Current Drug Discovery Technologies
|June 18, 2025
PubMed
Summary

Quantitative structure-property relationship (QSPR) modeling accurately predicts drug permeability for oral drug development. This in-silico approach screened 49,430 compounds, identifying 100 potential drug leads.

Keywords:
Caco-2 permeabilityQSPRdrug discoverydrug screening.

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Pharmacokinetics

Background:

  • Quantitative structure-property relationship (QSPR) modeling aids in predicting drug permeability through human intestinal enterocytes.
  • In-silico prediction of drug permeability is crucial for early-stage drug development and screening of potential drug candidates.

Purpose of the Study:

  • To develop a regression-based QSPR model for predicting Caco-2 cell permeability.
  • To utilize the developed QSPR model for virtual screening of a large compound database.

Main Methods:

  • A QSPR model was developed using a dataset of 1272 compounds and 30 selected 2D descriptors.
  • The model's predictive performance was evaluated using an R2 value of 0.96.
  • Virtual screening was performed on 49,430 antiviral compounds from the CAS database.

Main Results:

  • The developed QSPR model demonstrated high significance with an R2 value of 0.96.
  • Virtual screening identified 100 potential drug lead compounds.
  • Of the screened compounds, 96 fell within the model's Applicability Domain (AD), indicating reliable predictions.

Conclusions:

  • In-silico screening using QSPR models is a valuable tool for early-stage drug development.
  • The study successfully identified potential drug leads, accelerating the drug discovery process.
  • The developed QSPR model provides a robust method for predicting drug permeability.