ArticleScientific reports2019
An intact model for quantifying functional selectivity.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Analyzing lognormal data: A nonmathematical practical guide.Pharmacological reviews · 2025Review
- Quantitative assessment of ligand bias from bias plots: The bias coefficient "kappa".Biochimica et biophysica acta. General subjects · 2023Article
- Biased Ligands at the Kappa Opioid Receptor: Fine-Tuning Receptor Pharmacology.Handbook of experimental pharmacology · 2022Review
- Evaluation of the profiles of CBBritish journal of pharmacology · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A ligand that acts on a target receptor to activate particular multiple signalling pathways with activity that is distinct from other ligands is termed ligand bias. Quantification of ligand bias is based on applying the operational model to each pathway separately and subsequent calculation of the ligand bias metric (ΔΔlogR). This approach implies independence among different pathways and causes propagation of error in the calculation. Here, we propose a semi-mechanism-based model which allows for receptor selectivity across all the pathways simultaneously (termed the 'intact operational model'). The power of the intact model for detecting ligand bias was evaluated via stochastic simulation estimation studies. It was also applied to two examples: (1) opposing effects of Gi/Gs signalling of α2-adrenergic receptors and (2) simultaneous measurement of arachidonic acid release and inositol phosphate accumulation following 5-HT
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