ReviewIn silico prediction of blood–brain barrier permeation
Section snippets
Introduction to the blood-brain barrier
As its name suggests, the blood–brain barrier separates the brain and central nervous system (CNS) from the bloodstream. Clearly, for the great majority of drugs aimed at CNS targets, this barrier must be crossed for a therapeutic effect to be exerted, the only exceptions being compounds delivered by invasive or intranasal routes. Conversely, for non-CNS targets, passage across the BBB could lead to undesirable side effects and so should be minimized. One of the distinguishing features of the
Datasets available for in silico modeling
Datasets of sufficient size and quality are required to build predictive models. For the prediction of BBB permeation, there are various types of dataset that are available.
Types of in silico model available and current leaders in prediction accuracy
Based on either CNS+/CNS− or logBB datasets, various kinds of in silico prediction methods have been developed over the last decade or so (reviewed in Refs 9., 10., 11.). Here, some recent developments are highlighted.
What do logBB QSAR models tell us?
An examination of the descriptors (and their associated coefficients) that feature in the logBB QSAR models (summarized in Table 2), provides an insight into the molecular properties that determine brain permeation, a brief discussion of which follows.
Examples
To illustrate the aforementioned discussions with some brief examples, Table 3 collates three compounds that span the range of brain permeation, together with several computed physicochemical properties.
Trifluoroperazine is a good brain permeator (logBB = 1.44) and an examination of the calculated properties shows that the MW, PSA and logD are within the guidelines as explained earlier. Also, the Norinder ‘rule’ of ClogP – (N + O) gives a value of 2.19, which predicts a logBB >0. The pKa value
Conclusions and future directions
Over recent years, there has been a great deal of work seeking to generate predictive models for brain permeation. The few datasets that are available have been studied using a variety of molecular descriptors and statistical methods. State-of-the-art approaches seem able to achieve >80% correct classifications based on CNS+/CNS− data and predictions on small logBB test sets that approach experimental error (0.3–0.4 log units). Helpful insights into the molecular determinants of passive
Acknowledgements
We would like to thank Colin Bright (Argenta Discovery, http://www.argentadiscovery.com), who provided helpful advice on BBMEC assays. The comments of the anonymous referees were also much appreciated and contributed significantly to the development of this review.
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