# The ability of SABRE, a new quantitative receptor function model, to quantify receptor binding from even challenging concentration-effect data with a single unified fit

## TL;DR

This paper introduces a new model called SABRE that improves the analysis of receptor binding data using a single unified fit, even with challenging datasets.

## Contribution

The paper presents a new multiline model within the SABRE framework that enables reliable receptor binding analysis with a single global fit.

## Key findings

- The multiline SABRE model provides reliable results even with uncertain or challenging data.
- A six-model global fitting approach was found to be the most suitable for the analyzed data.
- The study highlights the importance of fitting SABRE equations effectively to functional data.

## Abstract

The Signal Amplification, Binding affinity, and Receptor-activation Efficacy (SABRE) model is the most recent general and quantitative model of receptor function. A specific extension of the SABRE model enables the determination of Kd (the equilibrium dissociation constant of the agonist-receptor complex) and q (the fraction of receptors remaining operable after pretreatment with an irreversible receptor antagonist) from exclusively functional data. In the present investigation, we reevaluated the concentration-effect (E/c) data of our related recent study on the SABRE model to assess the properties of our newly developed multiline model, inspired by professional criticism of our previous study in question. We have found this multiline model, constructed within the framework of the SABRE model, to be capable of providing reliable results via one global fitting (i.e., with a single unified fit), even for our somewhat challenging data (containing some uncertainty). The multiline model that proved to be the most suitable for the current data was a relatively complex, six-model global fitting. These results further emphasize the significance of finding the best way to fit the equations of the SABRE model to the functional data to be evaluated.