title: The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding.
abstract: OBJECTIVE
  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.
authors.affiliation: Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science & Engineering, Tongji University, 417 Mingjing Building, Shanghai 200092, China.
authors.forename: Xiang-Wei
authors.initials: XW
authors.lastname: Zhu
authors.forename: Alexander
authors.initials: A
authors.lastname: Sedykh
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Shu-Shen
authors.initials: SS
authors.lastname: Liu
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D001798:Blood Proteins / Q000378:metabolism*; D003198:Computer Simulation; D062313:Databases, Pharmaceutical; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
chemical_list: D001798:Blood Proteins; D004364:Pharmaceutical Preparations