The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding.

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.

SUBMITTER: Zhu XW 

PROVIDER: S-EPMC4363124 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature