Pharmaceutical Research

, Volume 30, Issue 7, pp 1790–1798

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

Research Paper

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.

KEY WORDS

%PPB ADMET drug fraction bound machine learning pharmacokinetics 

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

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science & EngineeringTongji UniversityShanghaiChina
  2. 2.Division of Chemical Biology and Medicinal Chemistry Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of ChemistryRutgers UniversityCamdenUSA
  4. 4.The Rutgers Center for Computational and Integrative BiologyRutgers UniversityCamdenUSA
  5. 5.100K Beard HallUniversity of North Carolina at Chapel HillChapel HillUSA

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