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Receptor kinetics: Binding flux-based approaches offer complementary insight into how ligand binding evolves with time.

Jul 2026 · British Journal of Pharmacology · 0 citations · 52 references
Medicine

TL;DR

The presented flux-based approaches have the advantage to address pre-equilibrium as well as equilibrium conditions and can be applied to any ligand-binding model.

Abstract

Background

AND

Purpose

Binding kinetics are essentially based on rate constants. Yet, this view has been challenged by the idea that 'binding fluxes' are dynamic and therefore more relevant. Those fluxes refer to the rate at which a target/receptor changes from one state into another through ligand/drug binding or a conformational change. Besides acting as building blocks for many algebraic expressions, they also determine how the concentration of each individual target state evolves over time. Here we show that such fluxes offer additional opportunities for understanding and predicting ligand binding. EXPERIMENTAL APPROACH Simulated binding data are obtained by solving the relevant set of flux-based differential equations for increasingly complex ligand binding models over very small time intervals by Euler's method. As input, they require only ligand concentration(s) and rate constants. KEY

Results

Compared to often-complex algebraic expressions, binding fluxes allow more intuitive/inductive insight into different aspects of ligand binding such as the occurrence of transient binding overshoots and the effect of a closing lid over the ligand's binding pocket on ligand dissociation. These examples disclose fundamental principles that govern ligand binding and, above all, they highlight the essential role of rate constants in all the examined binding models.

Conclusions

AND IMPLICATIONS Binding fluxes and rate constants complement each other: They respectively indicate how and why binding processes evolve in a certain fashion. The presented flux-based approaches have the advantage to address pre-equilibrium as well as equilibrium conditions and can be applied to any ligand-binding model.

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