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Prediction of drug absorption using multivariate statistics.

W J Egan1, K M Merz, J J Baldwin

  • 1Center for Informatics & Drug Discovery and Pharmacopeia Laboratories, Pharmacopeia, Inc., CN 5350, Princeton, New Jersey 08543-5350, USA. wegan@pharmacop.com

Journal of Medicinal Chemistry
|October 29, 2000
PubMed
Summary

A new model predicts passive intestinal absorption using physicochemical properties, identifying actively transported compounds. This tool aids in drug development by predicting oral bioavailability with 74-92% accuracy.

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Predicting oral drug absorption is crucial for drug development.
  • Existing models often lack interpretability or accuracy for complex absorption mechanisms.

Purpose of the Study:

  • To develop a statistical model for predicting passive intestinal absorption.
  • To identify compounds with active transport mechanisms.

Main Methods:

  • Utilized literature data on well- and poorly-absorbed compounds.
  • Employed pattern recognition and robust outlier detection.
  • Selected Polar Surface Area (PSA) and AlogP98 as key descriptors.

Main Results:

  • Developed a predictive model for passive intestinal absorption.

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  • Identified outliers as actively transported compounds.
  • Achieved 74-92% prediction accuracy on diverse datasets including drugs and Caco-2 permeability assays.

    • Conclusions:

    • The model effectively predicts passive intestinal absorption using interpretable descriptors.
    • PSA and AlogP98 are sufficient, rendering molecular weight superfluous.
    • The model aids in rational drug design and development by predicting oral bioavailability.