ArticleMolecules (Basel, Switzerland)2012
Computational prediction of blood-brain barrier permeability using decision tree induction.
Article in Molecules (Basel, Switzerland), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
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- Multifunctional effect of flavonoids fromHeliyon · 2023Article
- A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.Bioinformatics (Oxford, England) · 2023Article
- Neuroprotective Effects of Tryptanthrin-6-Oxime in a Rat Model of Transient Focal Cerebral Ischemia.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Corneal Permeability and Uptake of Twenty-Five Drugs: Species Comparison and Quantitative Structure-Permeability Relationships.Pharmaceutics · 2023Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.
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