|
Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. October 2005, Volume 4, Number 10, 737-750 |
A Predictive Model for Blood-Brain Barrier Penetration
Xuchun Fu, Zhifang Song, and Wenquan Liang
Internet Electron. J. Mol. Des. 2005, 4, 737-750
|
Abstract:
It is important to determine whether a candidate molecule is capable of
penetrating the blood-brain barrier in drug discovery and development.
The aim of this paper is to develop a predictive model for blood-brain
barrier penetration only using two simple descriptors, molecular
volume (V) and polar surface area (PSA, defined as the sum of the van
der Waals surface areas of oxygen atoms, nitrogen atoms, and attached
hydrogen atoms in a molecule). A data set of 100 compounds, which
was studied by other research groups, is divided into a training set of
61 compounds and two test sets (14 and 25 compounds). Molecular
volumes and polar surface areas are obtained from the molecular
conformations optimized using the semiempirical self-consistent field
molecular orbital calculation AM1 method. The model to predict
blood-brain barrier penetration from molecular volume and polar
surface area is derived on the training set using the stepwise multiple
regression analysis and then cross-validated using leave-one-out
procedure and tested on the external prediction. A logBB model is
developed using the training set of 61 compounds (4 compounds are
excluded as outliers):
logBB = -16.79(±3.28)V2 +11.24(±2.06)V
-2.249(±0.161)PSA - 0.6583(±0.2326)
(n = 57, r2 = 0.832, q2 = 0.804,
s = 0.329, F = 87.2), where logBB is the logarithm of the ratio of the
steady-state concentration of a compound in the brain to in the blood,
n is the number of compounds, r is the correlation coefficient, q is the
cross-validation coefficient, s is the standard deviation, F is the Fisher
F-statistic. The model is validated through two external test sets (14
compounds and 25 compounds). The root mean squared errors
(RMSE) are 0.599 for test set 1 of 14 compounds and 0.551 for test set
2 of 25 compounds. The simple model performs as well as other logBB
models developed using the same data set but different descriptors.
The model derived in this paper for the prediction of BBB penetration
shows a good predictive power. It shows that the hydrogen-bonding
potential, lipophilicity, and molecular size are important factors to
affect BBB penetration. The model is one of the simplest logBB
models and suitable for the rapid prediction of the BBB penetration for
a wide range of drug candidates.
|