Mathematical Modeling of G Protein-Coupled Receptor Function: What Can We Learn from Empirical and Mechanistic Models?

Abstract

Empirical and mechanistic models differ in their approaches to theanalysis of pharmacological effect. Whereas the parameters of the formerare not physical constants those of the latter embody the nature, oftencomplex, of biology. Empirical models are exclusively used for curvefitting, merely to characterize the shape of the E/{[}A] curves.Mechanistic models, on the contrary, enable the examination ofmechanistic hypotheses by parameter simulation. Regretfully, the manyparameters that mechanistic models may include can represent a greatdifficulty for curve fitting, representing, thus, a challenge forcomputational method development. In the present study some empiricaland mechanistic models are shown and the connections, which may appearin a number of cases between them, are analyzed from the curves theyyield. It may be concluded that systematic and careful curve shapeanalysis can be extremely useful for the understanding of receptorfunction, ligand classification and drug discovery, thus providing acommon language for the communication between pharmacologists andmedicinal chemists.