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

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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.

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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

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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.

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Authors and Affiliations

  1. The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey, 08102, USA

    Wenyi Wang, Marlene T. Kim & Hao Zhu

  2. Department of Chemistry, Rutgers University, 315 Penn St., Camden, New Jersey, 08102, USA

    Marlene T. Kim, Alexander Sedykh & Hao Zhu

  3. Multicase Inc., 23811 Chagrin Blvd, Suite 305, Beachwood, Ohio, 44122, USA

    Alexander Sedykh

Authors
  1. Wenyi Wang
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  2. Marlene T. Kim
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  4. Hao Zhu
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Corresponding author

Correspondence to Hao Zhu.

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Wang, W., Kim, M.T., Sedykh, A. et al. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling. Pharm Res 32, 3055–3065 (2015). https://doi.org/10.1007/s11095-015-1687-1

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  • DOI: https://doi.org/10.1007/s11095-015-1687-1

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