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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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A single unified model for fitting simple to complex receptor response data.

Peter Buchwald1

  • 1Department of Molecular and Cellular Pharmacology and Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA. pbuchwald@med.miami.edu.

Scientific Reports
|August 10, 2020
PubMed
Summary
This summary is machine-generated.

A new quantitative receptor model, SABRE, unifies the analysis of complex receptor-response data. It accurately fits diverse scenarios, including partial agonism and biased agonism, overcoming limitations of simpler models.

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Quantifying Agonist Activity at G Protein-coupled Receptors
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Area of Science:

  • Pharmacology
  • Biophysics
  • Computational Biology

Background:

  • Analyzing complex receptor-response data, where fractional response and occupancy diverge, presents significant challenges.
  • Existing models like Clark or Hill equations are inadequate for cases involving receptor reserve, partial agonism, biased agonism, or constitutive activity.
  • Operational models, while applicable, lack a unified approach, possess non-intuitive parameters, and struggle to integrate binding data.

Purpose of the Study:

  • To introduce and validate the Systems Approach to Biological Response Evaluation (SABRE) model for fitting complex receptor-response data.
  • To demonstrate SABRE's ability to unify the analysis of diverse pharmacological scenarios within a single framework.
  • To showcase the model's flexibility through reduced forms and a Hill-type extension for various experimental conditions.

Main Methods:

  • The study utilizes the quantitative receptor model SABRE, incorporating parameters for Signal Amplification (γ), Binding affinity (Kd), Receptor activation Efficacy (ε), and constitutive activity (εR0).
  • SABRE's unified equation was applied to fit simulated and experimental receptor-response data, ranging from simple to complex cases.
  • Reduced forms of SABRE, achieved by constraining parameters (e.g., εR0=0, γ=1, ε=1), and a Hill-type extension (n≠1) were employed to demonstrate model versatility.

Main Results:

  • The SABRE model successfully fitted complex receptor-response data, including scenarios with partial agonism, biased agonism, and constitutive activity, which are intractable for simpler models.
  • Constrained parameters within SABRE allowed for fitting simpler cases and demonstrated its ability to reduce to or connect with established models.
  • The Hill-type extension further enhanced SABRE's applicability to a broader range of experimental data.

Conclusions:

  • The SABRE model offers a unified and quantitative framework for analyzing complex receptor-response relationships, overcoming the limitations of traditional models.
  • Its flexibility in incorporating binding data and handling various pharmacological phenomena makes it a powerful tool for drug discovery and development.
  • SABRE provides a robust approach for accurately characterizing receptor behavior across a spectrum of experimental complexities.