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

Xiang Wei Zhu, Alexander Sedykh, Hao Zhu, Shu Shen Liu, Alexander Tropsha

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1790-1798
Number of pages9
JournalPharmaceutical Research
Issue number7
StatePublished - Jul 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Molecular Medicine
  • Pharmacology
  • Pharmaceutical Science
  • Organic Chemistry
  • Pharmacology (medical)


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