受体反应性方法(RRM)可以估计(甚至是可降解的)激动剂的浓度变化,在它的受体附近,通过曲线拟合(至少)稳定激动剂的两个浓度-效应(E/c)曲线。在此更改之前应生成一条曲线,然后是另一个,在同一系统中。因此,RRM产生替代参数(\“cx\”),因为稳定激动剂的浓度与其他激动剂的浓度变化等效。然而,回归可以通过几种方式进行,这会影响准确性,精度和易用性。该研究利用了先前离体研究的数据。通过进行个体(局部)或全局拟合,用RRM估计已知浓度的稳定激动剂。后者有一个或两个模型,使用对数(logcx)或非对数(cx)参数(后者在复杂或简化方程中),使用普通最小二乘或稳健回归,并以“一次全部”或“成对”的拟合方式。我们发现包含logcx的简化模型优于所有替代模型。最复杂的个体回归是最准确的,紧随其后的是中等复杂的双模型全局回归,然后是易于执行的单模型全局回归。双模型全局拟合是最精确的,其次是个体拟合(紧密)和单模型全局拟合(从远处)。成对拟合(一次两条E/c曲线)改进了估计。因此,双模型全局拟合,成对表演,并且建议将单个配件用于RRM,使用包含logcx的简化模型。
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.