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Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical

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This study developed classification models to predict drug absorption in the gastrointestinal tract (GIT) across various pH levels. These models accurately classify drug permeability, aiding in drug development and the biopharmaceutical classification system (BCS).

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Computational Chemistry and Cheminformatics
  • Pharmaceutical Sciences

Background:

  • Drug absorption in the gastrointestinal tract (GIT) is crucial for therapeutic efficacy.
  • Drug permeability is significantly influenced by pH-dependent ionization and regional GIT absorption.
  • Accurate prediction of drug permeability is essential for drug development and classification.

Purpose of the Study:

  • To develop and compare classification models for predicting drug permeability across a wide pH range.
  • To evaluate the performance of models using hydrophobicity (logP/logD) and theoretical molecular descriptors.
  • To improve the classification of drug substances into Biopharmaceutical Classification System (BCS) permeability classes.

Main Methods:

  • Utilized parallel artificial membrane permeability assay (PAMPA) data across four pH values (3, 5, 7.4, 9).
  • Developed logistic regression classification models using hydrophobicity descriptors (logP/logD) and theoretical molecular descriptors.
  • Employed triple validation and decision trees to assess and enhance model prediction accuracy for BCS classes.

Main Results:

  • Models demonstrated good classification and descriptive capabilities on training sets (accuracy: 0.76-0.91).
  • Validation sets (validation, external, and FDA reference drugs) showed strong prediction capabilities (accuracy: 0.72-0.91).
  • Decision trees integrated predictions, achieving higher accuracy (up to 0.91) for assigning BCS permeability classes.

Conclusions:

  • The developed classification models and decision trees are suitable for predicting permeability classes of passively transported drugs.
  • These models provide a valuable tool for drug substance classification within the BCS framework.
  • All models are publicly available in the QsarDB repository for broader scientific use.