Pharmaceutical Research

, 25:1836 | Cite as

New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules

  • Sandhya Kortagere
  • Dmitriy Chekmarev
  • William J. Welsh
  • Sean Ekins
Research Paper

Abstract

Purpose

The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood–brain barrier (BBB) modeling.

Materials and Methods

The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models.

Results

The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80–83% and leave-20%-out testing prediction accuracy between 80–82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs.

Conclusions

Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood–brain barrier permeation in drug discovery.

KEY WORDS

blood–brain barrier principal component analysis regression shape signatures support vector machine 

ABBREVIATIONS

ADME

absorption, distribution, metabolism and excretion

BBB

blood–brain barrier

CNS

central nervous system

MEP

molecular electrostatic potential

MOE

molecular operating environment

PCA

principal component analysis

P-gp

P-glycoprotein

QSAR

quantitative structure activity relationship

RFE

recursive feature elimination

SAS

solvent accessible surface

SVM

support vector machine

TPSA

topological polar surface area

UFS

unsupervised forward selection

Supplementary material

11095_2008_9584_MOESM1_ESM.doc (134 kb)
Supplemental Table 1Details the list of BBB datasets available in literature along with references. (DOC 134 KB)
11095_2008_9584_MOESM2_ESM.xls (67 kb)
Supplemental Table 2Provides model predictions for the SCUT database and consensus scoring respectively. Both are available online along with the SDF files for the Li, Combined and SCUT datasets. (XLS 67.0 KB)

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Sandhya Kortagere
    • 1
  • Dmitriy Chekmarev
    • 1
  • William J. Welsh
    • 1
  • Sean Ekins
    • 1
    • 2
    • 3
  1. 1.Department of Pharmacology and Environmental Bioinformatics and Computational Toxicology Center (ebCTC)University of Medicine & Dentistry of New Jersey (UMDNJ)–Robert Wood Johnson Medical SchoolPiscatawayUSA
  2. 2.Collaborations in ChemistryJenkintownUSA
  3. 3.Department of Pharmaceutical SciencesUniversity of MarylandBaltimoreUSA

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