The influence of the way of regression on the results obtained by the receptorial responsiveness method (RRM), a procedure to estimate a change in the concentration of a pharmacological agonist near the receptor

Abstract

The receptorial responsiveness method (RRM) enables the estimation of a change in concentration of an (even degradable) agonist, near its receptor, via curve fitting to (at least) two concentration-effect (E/c) curves of a stable agonist. One curve should be generated before this change, and the other afterwards, in the same system. It follows that RRM yields a surrogate parameter (“cx”) as the concentration of the stable agonist being equieffective with the change in concentration of the other agonist. However, regression can be conducted several ways, which can affect the accuracy, precision and ease-of-use. This study utilized data of previous ex vivo investigations. Known concentrations of stable agonists were estimated with RRM by performing individual (local) or global fitting, this latter with one or two model(s), using a logarithmic (logcx) or a nonlogarithmic (cx) parameter (the latter in a complex or in a simplified equation), with ordinary least-squares or robust regression, and with an “all-at-once” or “pairwise” fitting manner. We found that the simplified model containing logcx was superior to all alternative models. The most complicated individual regression was the most accurate, followed closely by the moderately complicated two-model global regression and then by the easy-to-perform one-model global regression. The two-model global fitting was the most precise, followed by the individual fitting (closely) and by the one-model global fitting (from afar). Pairwise fitting (two E/c curves at once) improved the estimation. Thus, the two-model global fitting, performed pairwise, and the individual fitting are recommended for RRM, using the simplified model containing logcx.