Methodological comparison of in vitro binding parameter estimation: sequential vs. simultaneous non-linear regression.
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.
Ernest
C Steven
CS
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. cse@lilly.com
Hooker
Andrew C
AC
Karlsson
Mats O
MO
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
D008024:Ligands; D011955:Receptors, Drug