Background:
Blood-brain barrier transport is an important process to be considered in drug
candidates. The blood-brain barrier protects the brain from toxicological agents and, therefore, also
establishes a restrictive mechanism for the delivery of drugs into the brain. Although there are different
and complex mechanisms implicated in drug transport, in this review we focused on the prediction of
passive diffusion through the blood-brain barrier.
Methods:
We elaborated on ligand-based and structure-based models that have been described to predict
the blood-brain barrier permeability.
Results:
Multiple 2D and 3D QSPR/QSAR models and integrative approaches have been published to
establish quantitative and qualitative relationships with the blood-brain barrier permeability. We explained
different types of descriptors that correlate with passive diffusion along with data analysis
methods. Moreover, we discussed the applicability of other types of molecular structure-based simulations,
such as molecular dynamics, and their implications in the prediction of passive diffusion. Challenges
and limitations of experimental measurements of permeability and in silico predictive methods
were also described.
Conclusion:
Improvements in the prediction of blood-brain barrier permeability from different types
of in silico models are crucial to optimize the process of Central Nervous System drug discovery and
development.
Blood-brain barrier permeability changes: nonlinear analysis of ECoG based on wavelet and machine learning approaches
