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QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

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Abstract

Purpose

Development of externally predictive Quantitative Structure–Activity Relationship (QSAR) models for Blood–Brain Barrier (BBB) permeability.

Methods

Combinatorial QSAR analysis was carried out for a set of 159 compounds with known BBB permeability data. All six possible combinations of three collections of descriptors derived from two-dimensional representations of molecules as chemical graphs and two QSAR methodologies have been explored. Descriptors were calculated by MolconnZ, MOE, and Dragon software. QSAR methodologies included k-Nearest Neighbors and Support Vector Machine approaches. All models have been rigorously validated using both internal and external validation methods.

Results

The consensus prediction for the external evaluation set afforded high predictive power (R 2 = 0.80 for 10 compounds within the applicability domain after excluding one activity outlier). Classification accuracies for two additional external datasets, including 99 drugs and 267 organic compounds, classified as permeable (BBB+) or non-permeable (BBB−) were 82.5% and 59.0%, respectively. The use of a fairly conservative model applicability domain increased the prediction accuracy to 100% and 83%, respectively (while naturally reducing the dataset coverage to 60% and 43%, respectively). Important descriptors that affect BBB permeability are discussed.

Conclusion

Models developed in these studies can be used to estimate the BBB permeability of drug candidates at early stages of drug development.

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Abbreviations

AD:

applicability domain

BBB:

blood–brain barrier

Combi-QSAR:

combinatorial QSAR

kNN:

k-nearest neighbors

MAE:

mean absolute error

NIH:

National Institutes of Health

OECD:

Organization for Economic Co-operation and Development

QSAR:

quantitative structure–activity relationship

SVM:

support vector machines

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Acknowledgments

We are grateful to Dr. Scott Oloff for his implementation of the SVM approach that was used in this study. We also thank Dr. J. Grier for his critical comments and his help with editing this manuscript. The studies reported in this paper have been supported by the NIH RoadMap grant GM076059.

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Authors and Affiliations

  1. The Laboratory for Molecular Modeling, School of Pharmacy, University of North Carolina at Chapel Hill, CB# 7360, Chapel Hill, North Carolina, 27599-7360, USA

    Liying Zhang, Hao Zhu, Alexander Golbraikh & Alexander Tropsha

  2. Division of Biocomputing, MSC11 6145, University of New Mexico School of Medicine, University of New Mexico, Albuquerque, New Mexico, 87131-0001, USA

    Tudor I. Oprea

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  1. Liying Zhang
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Correspondence to Alexander Tropsha.

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Zhang, L., Zhu, H., Oprea, T.I. et al. QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds. Pharm Res 25, 1902–1914 (2008). https://doi.org/10.1007/s11095-008-9609-0

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