The DOI matches its Crossref record.
A Simple Method for Quantifying Functional Selectivity and Agonist Bias
Open accessAbstract
Activation of seven-transmembrane (7TM) receptors by agonists does not always lead to uniform activation of all signaling pathways mediated by a given receptor. Relative to other ligands, many agonists are "biased" toward producing subsets of receptor behaviors. A hallmark of such "functional selectivity" is cell type dependence; this poses a particular problem for the profiling of agonists in whole cell test systems removed from the therapeutic one(s). Such response-specific cell-based variability makes it difficult to guide medicinal chemistry efforts aimed at identifying and optimizing therapeutically meaningful agonist bias. For this reason, we present a scale, based on the Black and Leff operational model, that contains the key elements required to describe 7TM agonism, namely, affinity (K(A) (-1)) for the receptor and efficacy (τ) in activating a particular signaling pathway. Utilizing a "transduction coefficient" term, log(τ/K(A)), this scale can statistically evaluate selective agonist effects in a manner that can theoretically inform structure-activity studies and/or drug candidate selection matrices. The bias of four chemokines for CCR5-mediated inositol phosphate production versus internalization is quantified to illustrate the practical application of this method. The independence of this method with respect to receptor density and the calculation of statistical estimates of confidence of differences are specifically discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Structural Features for Functional Selectivity at Serotonin Receptors
Wacker, D., Wang, C., Katritch, V., Han, G. W., Huang, X.-P., Vardy, E., McCorvy, J. D., Jiang, Y., Chu, M., Siu, F. Y., et al. (2013) · Science · doi:10.1126/science.1232808 · Cited by 664
Bias factor and therapeutic window correlate to predict safer opioid analgesics
Schmid, C. L., Kennedy, N. M., Ross, N. C., Lovell, K. M., Yue, Z., Morgenweck, J., Cameron, M. D., Bannister, T. D., & Bohn, L. M. (2017) · Cell · doi:10.1016/j.cell.2017.10.035 · Cited by 567
“The result of the comparison can be calculated as a ‘‘bias factor,’’ which essentially defines the extent of difference in relative agonist activity between two assays (the bias factor is 10̂DDLogt/KA(assay1-assay2)) (Kenakin et al., 2012).”
MethodHow Ligands Illuminate GPCR Molecular Pharmacology
Wacker, D., Stevens, R. C., & Roth, B. L. (2017) · Cell · doi:10.1016/j.cell.2017.07.009 · Cited by 560
Graaf, C. D., Donnelly, D., Wootten, D., Lau, J., Sexton, P. M., Miller, L. J., Ahn, J.-M., Liao, J., Fletcher, M. M., Yang, D., et al. (2016) · Pharmacological Reviews · doi:10.1124/pr.115.011395 · Cited by 403
Gillis, A., Gondin, A. B., Kliewer, A., Sanchez, J., Lim, H. D., Alamein, C., Manandhar, P., Santiago, M., Fritzwanker, S., Schmiedel, F., et al. (2020) · Science Signaling · doi:10.1126/scisignal.aaz3140 · Cited by 336
Metadata
- Authors
- Kenakin, T., Watson, C., Muniz-Medina, V., Christopoulos, A., & Novick, S.
- Venue
- ACS Chemical Neuroscience
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-01-23
- Volume
- 3
- Issue
- 3
- Pages
- 193-203
- ISSN
- 1948-7193 (ISSN-L)
- OpenAlex
- W138314167
- Cited by
- 484
- References
- 0
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Simple Method for Quantifying Functional Selectivity and Agonist Bias. https://app.cochat.ai/papers/10.1021/cn200111m
- The paper
- Kenakin, T., Watson, C., Muniz-Medina, V., Christopoulos, A., & Novick, S. (2012). A Simple Method for Quantifying Functional Selectivity and Agonist Bias. ACS Chemical Neuroscience. https://doi.org/10.1021/cn200111m
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.