ArticleBritish journal of pharmacology2018
A method for the quantification of biased signalling at constitutively active receptors.
Article in British journal of pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A critical re-evaluation of the slope factor of the operational model of agonism: When to exponentiate operational efficacy.Scientific reports · 2023Article
- Quantitative receptor model for responses that are left- or right-shifted versus occupancy (are more or less concentration sensitive): the SABRE approach.Frontiers in pharmacology · 2023Article
- A single unified model for fitting simple to complex receptor response data.Scientific reports · 2020Article
- 100 years of modelling ligand-receptor binding and response: A focus on GPCRs.British journal of pharmacology · 2020Review
- A kinetic method for measuring agonist efficacy and ligand bias using high resolution biosensors and a kinetic data analysis framework.Scientific reports · 2020Article
- The Murine CD137/CD137 Ligand Signalosome: A Signal Platform Generating Signal Complexity.Frontiers in immunology · 2020Review
- A Receptor Model With Binding Affinity, Activation Efficacy, and Signal Amplification Parameters for Complex Fractional Response Versus Occupancy Data.Frontiers in pharmacology · 2019Article
- A method for the quantification of biased signalling at constitutively active receptors.British journal of pharmacology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeBiased agonism, the ability of an agonist to differentially activate one of several signal transduction pathways when acting at a given receptor, is an increasingly recognized phenomenon at many receptors. The Black and Leff operational model lacks a way to describe constitutive receptor activity and hence inverse agonism. Thus, it is impossible to analyse the biased signalling of inverse agonists using this model. In this theoretical work, we develop and illustrate methods for the analysis of biased inverse agonism. EXPERIMENTAL APPROACH: Methods were derived for quantifying biased signalling in systems that demonstrate constitutive activity using the modified operational model proposed by Slack and Hall. The methods were illustrated using Monte Carlo simulations. KEY
resultsThe Monte Carlo simulations demonstrated that, with an appropriate experimental design, the model parameters are 'identifiable'. The method is consistent with methods based on the measurement of intrinsic relative activity (RA CONCLUSIONS AND IMPLICATIONS: In systems with constitutive activity, the Slack and Hall model provides methods for quantifying the absolute bias of agonists and inverse agonists. This provides an alternative to methods based on RA
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.