Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree InductionReport as inadecuate




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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





*

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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.

Keywords: blood brain barrier; drug transport; decision tree induction; QSAR modeling blood brain barrier; drug transport; decision tree induction; QSAR modeling





Author: Claudia Suenderhauf 1, Felix Hammann 1,2 and Jörg Huwyler 1,*

Source: http://mdpi.com/



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