Principles
Analysis of agonist action using the operational model

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    But how do we account for the relationship between receptor activation and the measured response? There is a simple solution to this problem known as the transducer function of the operational model and I cannot think of a better explanation of its theoretical basis than that given by Leff, one of its inventors45: “The operational model was derived using the simple piece of logic that if one knows about the beginning and end of a process, one can deduce what happens in the middle.”

  • Multiple active receptor conformation, agonist efficacy and maximum effect of the system: The conformation-based operational model of agonism

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    Although the latter assumption has been criticised [3] because it implies the use of an explicit relationship for an unknown post-receptor scenario, it has, in contrast to more-conservative null methods, the advantage of enabling a direct fitting to experimental E/[A] data [4]. It is worth emphasising that, notwithstanding the conceptual concerns derived for explicitly including a transducer function, the successful analysis by the operational model of E/[A] curves arising from many different receptor systems indicates that the model is of general applicability within the theoretical framework in which it was defined [5]. The operational model (Box 1; Equation IV) includes four parameters: KA, the agonist–receptor dissociation constant; τ, the operational efficacy of the agonist in the system; n, a parameter allowing for curves with slopes flatter or steeper than a rectangular hyperbola; and Em, the maximum effect of the system.

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    Scientists usually measure drug effects at a point downstream in the signaling pathway of a GPCR because it is difficult to measure receptor activation directly. Nonetheless, techniques have been developed to infer Kobs and a relative value of ɛ using a null method (i.e., a type of response-clamp analysis)6 or the operational model (i.e., reverse engineering).8,9 Both methods yield the same population parameters.

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