title: Methodological comparison of in vitro binding parameter estimation: sequential vs. simultaneous non-linear regression.
abstract: OBJECTIVE
  Analysis of simulated data was compared using sequential (NLR) and simultaneous non-linear regression (SNLR) to evaluate precision and accuracy of ligand binding parameter estimation.
  
  
  METHODS
  Commonly encountered experimental error, specifically residual error of binding measurements (RE), experiment-to-experiment variability (BEV) and non-specific binding (B(NS)), were examined for impact of parameter estimation using both methods. Data from equilibrium, dissociation, association and non-specific binding experiments were fit simultaneously (SNLR) using NONMEM VI compared to the common practice of analyzing data from each experiment separately and assigning these as exact values (NLR) for estimation of the subsequent parameters.
  
  
  RESULTS
  The greatest contributing factor to bias and variability in parameter estimation was RE of the measured concentrations of ligand bound; however, SNLR provided more accurate and less bias estimates. Subtraction of B(NS) from total ligand binding data provided poor estimation of specific ligand binding parameters using both NLR and SNLR. Additional methods examined demonstrated that the use of SNLR provided better estimation of specific binding parameters, whereas there was considerable bias using NLR. NLR cannot account for BEV, whereas SNLR can provide approximate estimates of BEV.
  
  
  CONCLUSIONS
  SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.
authors.affiliation: Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. cse@lilly.com
authors.forename: C Steven
authors.initials: CS
authors.lastname: Ernest
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Hooker
authors.forename: Mats O
authors.initials: MO
authors.lastname: Karlsson
mesh_terms: D015982:Bias; D003198:Computer Simulation; D008024:Ligands; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics; D011955:Receptors, Drug / Q000378:metabolism; D012044:Regression Analysis; D015203:Reproducibility of Results; D012984:Software
chemical_list: D008024:Ligands; D011955:Receptors, Drug