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Pharmaceutical Research

, Volume 32, Issue 9, pp 3055–3065 | Cite as

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

  • Wenyi Wang
  • Marlene T. Kim
  • Alexander Sedykh
  • Hao Zhu
Research Paper

ABSTRACT

Purpose

Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process.

Methods

We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models.

Results

The consensus QSAR models have R2 = 0.638 for five-fold cross-validation and R2 = 0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2 = 0.646 for five-fold cross-validation and R2 = 0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool.

Conclusions

The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models.

KEY WORDS

biological descriptors blood–brain barrier hybrid model permeability 

ABBREVIATIONS

5-HT

5-hydroxytryptamine

AD

Applicability domain

ADME

Absorption, distribution, metabolism, and excretion

AID

PubChem bio-assay identifier

ALDH1A1

Aldehyde dehydrogenase 1 family, member A1

AR

Androgen receptor

ASBT

Apical sodium-dependent bile acid transporter

BBB

Blood–brain barrier

BSEP

Bile salt export pump

CAMP

Cyclic adenosine monophosphate

CID

PubChem compound identifier

CNS

Central nervous system

ER-Alpha

Estrogen receptor alpha

HITs

Human intestinal transporters

HTS

High throughput screening

kNN

k-nearest neighbor

logBB

Logarithm of brain-plasma concentration ratio at steady-state

MAE

Mean absolute error

MCT

Monocarboxylic acid transporters

MDR

Multidrug resistance

MDR1

Multidrug resistance protein 1

MOE

Molecular operating environment software

MRP1

Multidrug resistance-associated protein 1

MRP3

Multidrug resistance-associated protein 3

MRP4

Multidrug resistance-associated protein 4

MRP5

Multidrug resistance-associated protein 5

OATPs

Organic anion transporting polypeptides

PCA

Principle component analysis

QSAR

Quantitative structure-activity relationship

RF

Random forest

SVM

Support vector machine

Notes

ACKNOWLEDGMENTS AND DISCLOSURES

Research reported in this publication was supported, in part, by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number R15ES023148 and the Colgate-Palmolive Grant for Alternative Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplementary material

11095_2015_1687_MOESM1_ESM.docx (1.6 mb)
Figure S1 (DOCX 1647 kb)
11095_2015_1687_MOESM2_ESM.xlsx (12 kb)
Table SI (XLSX 11 kb)
11095_2015_1687_MOESM3_ESM.xlsx (30 kb)
Table SII (XLSX 30 kb)
11095_2015_1687_MOESM4_ESM.xlsx (25 kb)
Table SIII (XLSX 24 kb)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Wenyi Wang
    • 1
  • Marlene T. Kim
    • 1
    • 2
  • Alexander Sedykh
    • 2
    • 3
  • Hao Zhu
    • 1
    • 2
  1. 1.The Rutgers Center for Computational and Integrative BiologyCamdenUSA
  2. 2.Department of ChemistryRutgers UniversityCamdenUSA
  3. 3.Multicase Inc.BeachwoodUSA

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