Affinity chromatography is the most widespread and widely accepted methodology for exploring drug-protein and protein-protein interactions. Despite the successful application of frontal analysis and zonal elution in affinity chromatography, research into the creation of new mathematical tools for data processing is encouraged due to these two methods' drawbacks of long analysis times and high ligand consumption. In this work, we created a novel mathematical model using the relationship between the molar amount of an injected solute and its capacity factor. We validated the method by analyzing the binding of drugs to human serum albumin (HSA) and β2-adrenoceptor (β2-AR). The association constants of omeprazole, propranolol and promethazine binding to HSA were determined to be (4.10±0.24)×10(4), (2.30±0.12)×10(4) and (1.24±0.14)×10(4)M(-1), respectively. These constants agreed with previously reported literature results of 4.60×10(4), 2.30×10(4) and 1.40×10(4)M(-1). Salbutamol, norepinephrine, isoprenaline, bamethane and methoxyphenamine were found to bind to β2-AR with association constants of (1.11±0.06)×10(3), (0.95±0.03)×10(3), (1.66±0.12)×10(3), (0.47±0.04)×10(3) and (0.43±0.02)×10(3)M(-1), respectively, which positively correlated to the negative logarithm of the dissociation constants obtained via radio-ligand binding assays. The proposed model is relatively fast and conserves ligand, and it has the potential to serve as an alternative method for rapidly revealing drug-protein and protein-protein interactions.
Keywords: Affinity chromatography; Beta(2)-adrenoceptor; Drug–protein interactions; Human serum albumin; Mathematical model.
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