Literature DB >> 21271563

QSAR analysis of blood-brain distribution: the influence of plasma and brain tissue binding.

Kiril Lanevskij1, Justas Dapkunas, Liutauras Juska, Pranas Japertas, Remigijus Didziapetris.   

Abstract

The extent of brain delivery expressed as steady-state brain/blood distribution ratio (log BB) is the most frequently used parameter for characterizing central nervous system exposure of drugs and drug candidates. The aim of the current study was to propose a physicochemical QSAR model for log BB prediction. Model development involved the following steps: (i) A data set consisting of 470 experimental log BB values determined in rodents was compiled and verified to ensure that selected data represented drug disposition governed by passive diffusion across blood-brain barrier. (ii) Available log BB values were corrected for unbound fraction in plasma to separate the influence of drug binding to brain and plasma constituents. (iii) The resulting ratios of total brain to unbound plasma concentrations reflecting brain tissue binding were described by a nonlinear ionization-specific model in terms of octanol/water log P and pK(a). The results of internal and external validation demonstrated good predictive power of the obtained model as both log BB and brain tissue binding strength were predicted with residual mean square error of 0.4 log units. The statistical parameters were similar among training and validation sets, indicating that the model is not likely to be overfitted.
Copyright © 2011 Wiley-Liss, Inc.

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Year:  2011        PMID: 21271563     DOI: 10.1002/jps.22442

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  12 in total

1.  Getting the MAX out of Computational Models: The Prediction of Unbound-Brain and Unbound-Plasma Maximum Concentrations.

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2.  The activity of organic anion transporter-3: Role of dexamethasone.

Authors:  Haoxun Wang; Chenchang Liu; Guofeng You
Journal:  J Pharmacol Sci       Date:  2018-02-02       Impact factor: 3.337

3.  Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

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Journal:  AAPS J       Date:  2018-03-21       Impact factor: 4.009

Review 4.  Prediction of drug disposition on the basis of its chemical structure.

Authors:  David Stepensky
Journal:  Clin Pharmacokinet       Date:  2013-06       Impact factor: 6.447

Review 5.  In silico prediction of brain exposure: drug free fraction, unbound brain to plasma concentration ratio and equilibrium half-life.

Authors:  Morena Spreafico; Matthew P Jacobson
Journal:  Curr Top Med Chem       Date:  2013       Impact factor: 3.295

6.  Predictivity approach for quantitative structure-property models. Application for blood-brain barrier permeation of diverse drug-like compounds.

Authors:  Sorana D Bolboacă; Lorentz Jäntschi
Journal:  Int J Mol Sci       Date:  2011-07-05       Impact factor: 5.923

7.  A high-throughput cell-based method to predict the unbound drug fraction in the brain.

Authors:  André Mateus; Pär Matsson; Per Artursson
Journal:  J Med Chem       Date:  2014-03-20       Impact factor: 7.446

8.  Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.

Authors:  Eugene V Radchenko; Alina S Dyabina; Vladimir A Palyulin
Journal:  Molecules       Date:  2020-12-13       Impact factor: 4.411

9.  A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors.

Authors:  Fanwang Meng; Yang Xi; Jinfeng Huang; Paul W Ayers
Journal:  Sci Data       Date:  2021-10-29       Impact factor: 6.444

10.  A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.

Authors:  Daqing Zhang; Jianfeng Xiao; Nannan Zhou; Mingyue Zheng; Xiaomin Luo; Hualiang Jiang; Kaixian Chen
Journal:  Biomed Res Int       Date:  2015-10-04       Impact factor: 3.411

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