Department of Molecular and Cellular Pharmacology and Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.
Sci Rep. 2022 Nov 6;12(1):18833. doi: 10.1038/s41598-022-23588-w.
Methods that allow quantification of receptor binding (occupancy) by measuring response (effect) data only are of interest as they can be used to allow characterization of binding properties (e.g., dissociation constant, K) without having to perform explicit ligand binding experiments that require different setups (e.g., use of labeled ligands). However, since response depends not just on the binding affinity-determined receptor occupancy, but also on receptor activation, which is affected by ligand efficacy (plus constitutive activity, if present), and downstream pathway amplification, this requires the acquisition and fitting of multiple concentration-response data. Here, two alternative methods, which both are straightforward to implement using nonlinear regression software, are described to fit such multiple responses measured at different receptor levels that can be obtained, for example, by partial irreversible receptor inactivation (i.e., Furchgott method) or different expression levels. One is a simple method via straightforward fitting of each response with sigmoid functions and estimation of K from the obtained E and EC values as K = (E·EC' - E'·EC)/(E - E'). This is less error-prone than the original Furchgott method of double-reciprocal fit and simpler than alternatives that require concentration interpolations, thus, should allow more widespread use of this so-far underutilized approach to estimate binding properties. Relative efficacies can then be compared using E·K/EC values. The other is a complex method that uses the SABRE receptor model to obtain a unified fit of the multiple concentration-response curves with a single set of parameters that include binding affinity K, efficacy ε, amplification γ, and Hill coefficient n. Illustrations with simulated and experimental data are presented including with activity data of three muscarinic agonists measured in rabbit myocardium.
方法允许通过仅测量响应(效应)数据来量化受体结合(占有率),这很有趣,因为它们可以用于在不必进行需要不同设置(例如,使用标记配体)的明确配体结合实验的情况下,表征结合特性(例如,解离常数,K)。然而,由于响应不仅取决于结合亲和力决定的受体占有率,还取决于受体激活,而受体激活受配体效力(加上存在的组成性活性)和下游途径放大的影响,这需要获取和拟合多个浓度-响应数据。在这里,描述了两种替代方法,它们都可以使用非线性回归软件直接实现,用于拟合可以获得的不同受体水平测量的多个响应,例如通过部分不可逆受体失活(即 Furchgott 方法)或不同的表达水平。一种是简单的方法,通过直接用 sigmoid 函数拟合每个响应,并从获得的 E 和 EC 值估计 K,即 K =(E·EC' - E'·EC)/(E - E')。与原始 Furchgott 双倒数拟合方法相比,这种方法错误更少,比需要浓度插值的替代方法更简单,因此,应该允许更广泛地使用这种迄今为止未充分利用的方法来估计结合特性。然后可以使用 E·K/EC 值比较相对效力。另一种是复杂的方法,它使用 SABRE 受体模型对多个浓度-响应曲线进行统一拟合,使用一组包括结合亲和力 K、效力 ε、放大 γ 和 Hill 系数 n 的参数。使用模拟和实验数据进行了说明,包括在兔心肌中测量的三种毒蕈碱激动剂的活性数据。