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The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding

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Abstract

Purpose

To develop accurate in silico predictors of Plasma Protein Binding (PPB).

Methods

Experimental PPB data were compiled for over 1,200 compounds. Two endpoints have been considered: (1) fraction bound (%PPB); and (2) the logarithm of a pseudo binding constant (lnKa) derived from %PPB. The latter metric was employed because it reflects the PPB thermodynamics and the distribution of the transformed data is closer to normal. Quantitative Structure-Activity Relationship (QSAR) models were built with Dragon descriptors and three statistical methods.

Results

Five-fold external validation procedure resulted in models with the prediction accuracy (R2) of 0.67 ± 0.04 and 0.66 ± 0.04, respectively, and the mean absolute error (MAE) of 15.3 ± 0.2% and 13.6 ± 0.2%, respectively. Models were validated with two external datasets: 173 compounds from DrugBank, and 236 chemicals from the US EPA ToxCast project. Models built with lnKa were significantly more accurate (MAE of 6.2–10.7 %) than those built with %PPB (MAE of 11.9–17.6 %) for highly bound compounds both for the training and the external sets.

Conclusions

The pseudo binding constant (lnKa) is more appropriate for characterizing PPB binding than conventional %PPB. Validated QSAR models developed herein can be applied as reliable tools in early drug development and in chemical risk assessment.

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Abbreviations

%PPB:

Percent plasma protein binding

5FCV:

5-fold external cross-validation

AD:

Applicability domain

ADMET:

Absorption, distribution, metabolism, excretion, and toxicity

CCR:

Correct classification rate (balanced classification accuracy)

HSA:

Human serum albumin

kNN:

k nearest neighbors

lnKa:

Natural logarithm of the pseudo binding constant imputed from %PPB

LogP:

Octanol-water partition coefficient

LOO-CV:

Leave-one-out cross validation

MAE:

Mean absolute error

QSAR:

Qualitative structure-activity relationship

R2 :

Coefficient of determination

RED:

Rapid equilibrium dialysis

RF:

Random forest

RMSE:

Root mean square error

SVM:

Support vector machine

Tc:

Tanimoto similarity coefficient

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ACKNOWLEDGMENTS and DISCLOSURES

We thank Drs. Alexander Golbraikh and Denis Fourches for their useful comments on this study. This work was supported, in part, by grants from National Institutes of Health (GM66940 and R21GM076059), The Johns Hopkins Center for Alternatives to Animal Testing (20011-21), and the National Natural Science Foundation of China (20977065).

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Authors and Affiliations

  1. Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science & Engineering, Tongji University, 417 Mingjing Building, Shanghai, 200092, China

    Xiang-Wei Zhu & Shu-Shen Liu

  2. Division of Chemical Biology and Medicinal Chemistry Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27514, USA

    Xiang-Wei Zhu, Alexander Sedykh, Hao Zhu & Alexander Tropsha

  3. Department of Chemistry, Rutgers University, 315 Penn Street, Camden, New Jersey, 08102, USA

    Hao Zhu

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

    Hao Zhu

  5. 100K Beard Hall, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7568, USA

    Alexander Tropsha

Authors
  1. Xiang-Wei Zhu
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  2. Alexander Sedykh
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  3. Hao Zhu
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  4. Shu-Shen Liu
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Corresponding authors

Correspondence to Shu-Shen Liu or Alexander Tropsha.

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Zhu, XW., Sedykh, A., Zhu, H. et al. The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding. Pharm Res 30, 1790–1798 (2013). https://doi.org/10.1007/s11095-013-1023-6

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  • DOI: https://doi.org/10.1007/s11095-013-1023-6

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