Recent advances in computational prediction of drug absorption and permeability in drug discovery

Tingjun Hou, Junmei Wang, Wei Zhang, Wei Wang, Xiaojie Xu

Research output: Contribution to journalReview articlepeer-review

142 Scopus citations

Abstract

Approximately 40%-60% of developing drugs failed during the clinical trials because of ADME/Tox deficiencies. Virtual screening should not be restricted to optimize binding affinity and improve selectivity; and the pharmacokinetic properties should also be included as important filters in virtual screening. Here, the current development in theoretical models to predict drug absorption-related properties, such as intestinal absorption, Caco-2 permeability, and blood-brain partitioning are reviewed. The important physicochemical properties used in the prediction of drug absorption, and the relevance of predictive models in the evaluation of passive drug absorption are discussed. Recent developments in the prediction of drug absorption, especially with the application of new machine learning methods and newly developed software are also discussed. Future directions for research are outlined.

Original languageEnglish (US)
Pages (from-to)2653-2667
Number of pages15
JournalCurrent Medicinal Chemistry
Volume13
Issue number22
DOIs
StatePublished - Sep 2006

Keywords

  • ADME
  • Blood-brain partitioning (BBB)
  • Caco-2 monolayer
  • Drug adsorption
  • LogBB
  • Permeability
  • QSAR

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Organic Chemistry

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