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• W2034705752 abstract "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." @default.
• W2034705752 created "2016-06-24" @default.
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• W2034705752 date "2013-04-09" @default.
• W2034705752 modified "2023-10-07" @default.
• W2034705752 title "The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding" @default.
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• W2034705752 doi "https://doi.org/10.1007/s11095-013-1023-6" @default.
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