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

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 (R(2)) 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. CONCLUSION: 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