Predicting Blood−Brain Barrier Permeation from Three-Dimensional Molecular Structure

Abstract

Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds.

Simple chalcones and bis-chalcones ethers as possible pleiotropic agents

“…Bloodbrain barrier (BBB) 33 permeation and Caco-2 cell permeability 34 of the studied compounds were predicted using VolSurf (version 4, version 4.1.4, Molecular Discovery Ltd., 2005; www. moldiscovery.com).…”

Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms

“…Ajay et al (1999) used a database of over 9,000 molecules and achieved an 80% predictivity. The prediction methods of Luco (1999) and Crivori (2000) based on threedimensional structure analysis both achieved around 90% predictivity. The SVM algorithm is impressive because even with a very small data set it performs comparably to the other methods used.…”

“…The input set consists of molecular weight, molecular volume, surface area, the percent of the surface area that is hydrophilic, the log P (octanol/water partitioning coef cient), the number of hydrogen bond donors, the number of hydrogen bond acceptors, the hydrophilic/lipophilic balance, and a three-dimensional representation of the number of hydrogen bonds. These variables were decided on based on the parameters previously determined to be important in BBB transport (Pardridge, 1998;Crivori et al, 2000;Fischer et al, 1998) as well as on the information available from the ChemSW database. Passive diffusion is the primary method of transport looked at in this study, and each of these variables is important in determining the ability of a molecule to diffuse through a lipid bilayer.…”

Molecular interaction fields vs. quantum-mechanical-based descriptors in the modelling of lipophilicity of platinum(iv) complexes

“…VolSurf descriptors. 33,34 Briefly, VolSurf is a computational procedure to find out molecular descriptors from 3D molecular interaction fields (MIFs) 35 Since VolSurf descriptors represent polarity and hydrophobicity (as well as size and shape) of molecules, they are generally well suited for the modelling of lipophilicity indexes. 39 This was shown in previous studies where robust QSRR models based on VolSurf descriptors were used to model the variation of 35 different chromatographic lipophilicity indexes.…”