In classical pharmacology, bioassay data are fit to general equations (e.g. the dose response equation) to determine empirical drug parameters (e.g. EC₅₀ and E_max), which are then used to calculate chemical parameters such as affinity and efficacy. Here we used a similar approach for kinetic, time course signaling data, to allow empirical and chemical definition of signaling by G-protein-coupled receptors in kinetic terms. Experimental data are analyzed using general time course equations (model-free approach) and mechanistic model equations (mechanistic approach) in the commonly-used curve-fitting program, GraphPad Prism. A literature survey indicated signaling time course data usually conform to one of four curve shapes: the straight line, association exponential curve, rise-and-fall to zero curve, and rise-and-fall to steady-state curve. In the model-free approach, the initial rate of signaling is quantified and this is done by curve-fitting to the whole time course, avoiding the need to select the linear part of the curve. It is shown that the four shapes are consistent with a mechanistic model of signaling, based on enzyme kinetics, with the shape defined by the regulation of signaling mechanisms (e.g. receptor desensitization, signal degradation). Signaling efficacy is the initial rate of signaling by agonist-occupied receptor (k_τ), simply the rate of signal generation before it becomes affected by regulation mechanisms, measurable using the model-free analysis. Regulation of signaling parameters such as the receptor desensitization rate can be estimated if the mechanism is known. This study extends the empirical and mechanistic approach used in classical pharmacology to kinetic signaling data, facilitating optimization of new therapeutics in kinetic terms.

中文翻译：

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分析G蛋白偶联受体的动力学信号数据

在经典药理学中，将生物测定数据与通用方程式（例如剂量响应方程式）拟合以确定经验药物参数（例如EC ₅₀和E _max），然后用于计算化学参数，例如亲和力和功效。在这里，我们对动力学，时间过程的信号数据使用了类似的方法，以经验和化学方式定义了G蛋白偶联受体的动力学信号。在通用的曲线拟合程序GraphPad Prism中，使用通用时程方程（无模型方法）和机械模型方程（机械方法）分析实验数据。文献调查表明，信号时程数据通常符合以下四种曲线形状之一：直线，关联指数曲线，上升和下降到零曲线以及上升和下降到稳态曲线。在无模型方法中，信号的初始速率会被量化，这可以通过对整个时间过程进行曲线拟合来实现，避免选择曲线的线性部分。结果表明，这四个形状与基于酶动力学的信号传导机理模型相吻合，其形状由信号传导机理的调节（例如受体脱敏，信号降解）决定。信号传递效能是激动剂占据的受体（ķ _τ），只是之前它变得被调节机制使用无模型分析的影响，可测量的信号生成的速率。如果机理已知，则可以估计信号传导参数的调节，例如受体脱敏率。这项研究将经典药理学中使用的经验和机制方法扩展到动力学信号数据，从而促进了动力学方面新疗法的优化。