title: Simulating the influence of plasma protein on measured receptor affinity in biochemical assays reveals the utility of Schild analysis for estimating compound affinity for plasma proteins.
abstract: OBJECTIVE
  Plasma protein binding (PPB) influences the free fraction of drug available to bind to its target and is therefore an important consideration in drug discovery. While traditional methods for assessing PPB (e.g. rapid equilibrium dialysis) are suitable for comparing compounds with relatively weak PPB, they are not able to accurately discriminate between highly bound compounds (typically >99.5%). The aim of the present work was to use mathematical modelling to explore the potential utility of receptor binding and cellular functional assays to estimate the affinity of compounds for plasma proteins. Plasma proteins are routinely added to in vitro assays, so a secondary goal was to investigate the effect of plasma proteins on observed ligand-receptor interactions.
  
  
  METHODS
  Using the principle of conservation of mass and the law of mass action, a cubic equation was derived describing the ligand-receptor complex [LR] in the presence of plasma protein at equilibrium.
  
  
  RESULTS
  The model demonstrates the profound influence of PPB on in vitro assays and identifies the utility of Schild analysis, which is usually applied to determine receptor-antagonist affinities, for calculating affinity at plasma proteins (termed KP ). We have also extended this analysis to functional effects using operational modelling and demonstrate that these approaches can also be applied to cell-based assay systems.
  
  
  CONCLUSIONS
  These mathematical models can potentially be used in conjunction with experimental data to estimate drug-plasma protein affinities in the earliest phases of drug discovery programmes.
authors.affiliation: Novartis Institutes for Biomedical Research, Horsham, West Sussex, UK.
authors.forename: D
authors.initials: D
authors.lastname: Blakeley
authors.affiliation: School of Life Sciences, University of Nottingham, Nottingham, UK.
authors.forename: D A
authors.initials: DA
authors.lastname: Sykes
authors.affiliation: School of Life Sciences, University of Nottingham, Nottingham, UK.
authors.forename: P
authors.initials: P
authors.lastname: Ensor
authors.affiliation: Roche Innovation Center Basel, Switzerland.
authors.forename: E
authors.initials: E
authors.lastname: Bertran
authors.affiliation: Department of Mathematics, University of Surrey, Guildford, UK.
authors.forename: P J
authors.initials: PJ
authors.lastname: Aston
authors.affiliation: School of Life Sciences, University of Nottingham, Nottingham, UK.
authors.forename: S J
authors.initials: SJ
authors.lastname: Charlton
mesh_terms: D001798:Blood Proteins / Q000378:metabolism*; D008024:Ligands; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D011485:Protein Binding; D011955:Receptors, Drug / Q000378:metabolism
chemical_list: D001798:Blood Proteins; D008024:Ligands; D004364:Pharmaceutical Preparations; D011955:Receptors, Drug