Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction
1
Division of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, CH-4056 Basel, Switzerland
2
Psychiatric Hospital of the University of Basel, Wilhelm-Klein-Str. 27, 4012 Basel, Switzerland
*
Author to whom correspondence should be addressed.
Molecules 2012, 17(9), 10429-10445; https://doi.org/10.3390/molecules170910429
Received: 8 June 2012
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Revised: 17 August 2012
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Accepted: 27 August 2012
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Published: 31 August 2012
(This article belongs to the Special Issue QSAR and Its Applications)
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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MDPI and ACS Style
Suenderhauf, C.; Hammann, F.; Huwyler, J. Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction. Molecules 2012, 17, 10429-10445. https://doi.org/10.3390/molecules170910429
AMA Style
Suenderhauf C, Hammann F, Huwyler J. Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction. Molecules. 2012; 17(9):10429-10445. https://doi.org/10.3390/molecules170910429
Chicago/Turabian StyleSuenderhauf, Claudia, Felix Hammann, and Jörg Huwyler. 2012. "Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction" Molecules 17, no. 9: 10429-10445. https://doi.org/10.3390/molecules170910429
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