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.
Highlights
Experimental determination of blood-brain barrier (BBB) permeability for small molecules is notoriously difficult
With respect to drug uptake into the central nervous system, comprehensive datasets have been established based on in vivo pharmacokinetic studies in which brain exposure is determined after intravenous peripheral administration of a test compound [9,31]
This method has the advantage of high sensitivity, as there is no systemic exposure of the test compound prior to its transport across the blood-brain barrier (BBB)
Summary
Experimental determination of blood-brain barrier (BBB) permeability for small molecules is notoriously difficult. The volume cleared per unit time is designated as the BBB permeability-surface area (PS) product (logPS), a parameter obtained from in situ brain perfusion studies in which a (radiolabeled) test compound is directly injected into the internal carotid artery [1,2,3]. This procedure is considered superior to other methods such as blood to brain drug partition measurements at steady state (logBB), as it lacks systemic distribution effects, which distort brain penetration substantially [4]. According to Fick’s law of diffusion, the rate of passive diffusion of a small molecule across a phospholipid membrane will be proportional to the partition coefficient of the drug between the membrane and the external medium, the diffusion coefficient of the drug within the membrane and the concentration gradient across the membrane [10]
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