A two-state model for the kinetics of competitive radioligand binding

作者：Guo, Dong; Peletier, Lambertus A.; Bridge, Lloyd; Keur, Wesley; de Vries, Henk; Zweemer, Annelien; Heitman, Laura H.; IJzerman, Adriaan P.^*

来源：British Journal of Pharmacology, 2018, 175(10): 1719-1730.

DOI：10.1111/bph.14184

• PROTEIN-COUPLED RECEPTORS
• ADENOSINE A(1) RECEPTOR
• TARGET RESIDENCE TIME
• CONCISE GUIDE
• LEAD OPTIMIZATION
• DISSOCIATION RATE
• DRUG
• LIGAND
• ANTAGONISTS
• AGONISTS

BACKGROUND AND PURPOSE
Ligand-receptor binding kinetics is receiving increasing attention in the drug research community. The Motulsky and Mahan model, a one-state model, offers a method for measuring the binding kinetics of an unlabelled ligand, with the assumption that the labelled ligand has no preference while binding to distinct states or conformations of a drug target. As such, the one-state model is not applicable if the radioligand displays biphasic binding kinetics to the receptor.
EXPERIMENTAL APPROACH
We extended the Motulsky and Mahan model to a two-state model, in which the kinetics of the unlabelled competitor binding to different receptor states (R-1 and R-2) can be measured. With this extended model, we determined the binding kinetics of unlabelled N-5-ethylcarboxamidoadenosine (NECA), a representative agonist for the adenosine A(1) receptor. Subsequently, an application of the model was exemplified by measuring the binding kinetics of other A(1) receptor ligands. In addition, limitations of the model were investigated as well.
KEY RESULTS
The kinetic rate constants of unlabelled NECA were comparable with the results of kinetic radioligand binding assays in which [H-3]-NECA was used. The model was further validated by good correlation between simulated results and the experimental data.
CONCLUSION
The two-state model is sufficient to analyse the binding kinetics of an unlabelled ligand, when a radioligand shows biphasic association characteristics. We expect this two-state model to have general applicability for other targets as well.

• 出版日期2018-5
• 单位徐州医科大学; MIT