Aim: To generate and validate predictive models for blood-brain permeation (BBB) of
CNS molecules using the QSPR approach.
Background: Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive
models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives
for determining BBB permeation by experimentation.
Objective: The objective of the present study was to generate QSPR models for the permeation of
CNS molecules across BBB and its validation using existing in-house leads.
Methods: The present study envisaged the determination of the set of molecular descriptors which
are considered significant correlative factors for BBB permeation property. Quantitative Structure-
Property Relationship (QSPR) approach was followed to describe the correlation between identified
descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular
descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area,
etc. of molecules that served the highest correlation with BBB permeation. The experimental
data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation
using a regression analysis method like Multiple Linear Regression (MLR).
Results: The best QSPR model was Model 3, which exhibited regression coefficient as R2= 0.89,
F = 36; Q2= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity,
etc., which were considered key parameters in the determination of the BBB permeability.
The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and
correlation studies were conducted between experimental and predicted BBB permeability.
Conclusion: The QSPR model 3 showed predictive results that were in good agreements with experimental
results for blood-brain permeation. Thus, this model was found to be satisfactory in
achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines
and tricyclic compounds.