The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding
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To develop accurate in silico predictors of Plasma Protein Binding (PPB).
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.
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.
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
Percent plasma protein binding
5-fold external cross-validation
Absorption, distribution, metabolism, excretion, and toxicity
Correct classification rate (balanced classification accuracy)
Human serum albumin
k nearest neighbors
Natural logarithm of the pseudo binding constant imputed from %PPB
Octanol-water partition coefficient
Leave-one-out cross validation
Mean absolute error
Qualitative structure-activity relationship
Coefficient of determination
Rapid equilibrium dialysis
Root mean square error
Support vector machine
Tanimoto similarity coefficient
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