The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
The role of kinetic context in apparent biased agonism at GPCRs
Abstract
Biased agonism describes the ability of ligands to stabilize different conformations of a GPCR linked to distinct functional outcomes and offers the prospect of designing pathway-specific drugs that avoid on-target side effects. This mechanism is usually inferred from pharmacological data with the assumption that the confounding influences of observational (that is, assay dependent) and system (that is, cell background dependent) bias are excluded by experimental design and analysis. Here we reveal that ‘kinetic context’, as determined by ligand-binding kinetics and the temporal pattern of receptor-signalling processes, can have a profound influence on the apparent bias of a series of agonists for the dopamine D 2 receptor and can even lead to reversals in the direction of bias. We propose that kinetic context must be acknowledged in the design and interpretation of studies of biased agonism.
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
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Yung-Chi, C., & Prusoff, W. H. (1973) · Biochemical Pharmacology · doi:10.1016/0006-2952(73)90196-2 · Cited by 12,922
Abdi, H., & Williams, L. J. (2010) · WIREs Computational Statistics · doi:10.1002/wics.101 · Cited by 11,104
How many drug targets are there?
Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2199 · Cited by 3,505
A Genome-Wide Association Search for Type 2 Diabetes Genes in African Americans
Palmer, N., McDonough, C. W., Hicks, P. J., Roh, B. H., Wing, M. R., An, S. S., Hester, J. M., Cooke, J. N., Bostrom, M. A., Rudock, M., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029202 · Cited by 1,619
Exploring polyethylenimine‐mediated DNA transfection and the proton sponge hypothesis
Akinc, A., Thomas, M., Klibanov, A. M., & Langer, R. (2005) · The Journal of Gene Medicine · doi:10.1002/jgm.696 · Cited by 1,337
Functional Selectivity and Classical Concepts of Quantitative Pharmacology
Urban, J. D., Clarke, W. P., von Zastrow, M., Nichols, D. E., Kobilka, B., Weinstein, H., Javitch, J. A., Roth, B. L., Christopoulos, A., Sexton, P. M., et al. (2007) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.106.104463 · Cited by 1,116
Self-citationOperational models of pharmacological agonism
Black, J. W., & Leff, P. (1983) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1983.0093 · Cited by 951
Conformational biosensors reveal GPCR signalling from endosomes
Irannejad, R., Tomshine, J. C., Tomshine, J. R., Chevalier, M., Mahoney, J. P., Steyaert, J., Rasmussen, S. G. F., Sunahara, R. K., El-Samad, H., Huang, B., et al. (2013) · Nature · doi:10.1038/nature12000 · Cited by 819
β-Arrestin-dependent, G Protein-independent ERK1/2 Activation by the β2 Adrenergic Receptor
Shenoy, S. K., Drake, M. T., Nelson, C. D., Houtz, D. A., Xiao, K., Madabushi, S., Reiter, E., Premont, R. T., Lichtarge, O., & Lefkowitz, R. J. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m506576200 · Cited by 759
Research works printed but not linked: 1
- Kenakin, T., Watson, C., Muniz-Medina, V., Christopoulos, A. & Novick, S. A simple method for quantifying functional selectivity and agonist bias. ACS Chem. Neurosci. 3, 193–203 (2012). · DOI · Paper page
Cited by
How 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
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
Self-citationCommunity guidelines for GPCR ligand bias: IUPHAR review 32
Kolb, P., Kenakin, T., Alexander, S. P. H., Bermudez, M., Bohn, L. M., Breinholt, C. S., Bouvier, M., Hill, S. J., Kostenis, E., Martemyanov, K. A., et al. (2022) · British Journal of Pharmacology · doi:10.1111/bph.15811 · Cited by 232
Sykes, D. A., Moore, H., Stott, L., Holliday, N., Javitch, J. A., Lane, J. R., & Charlton, S. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00716-z · Cited by 218
Self-citationAllostery and biased agonism at class B G protein-coupled receptors
Wootten, D., Miller, L. J., Koole, C., Christopoulos, A., & Sexton, P. M. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00049 · Cited by 112
Self-citationLow Intrinsic Efficacy Alone Cannot Explain the Improved Side Effect Profiles of New Opioid Agonists
Stahl, E. L., & Bohn, L. M. (2022) · Biochemistry · doi:10.1021/acs.biochem.1c00466 · Cited by 55
Vasudevan, L., Vandeputte, M., Deventer, M., Wouters, E., Cannaert, A., & Stove, C. P. (2020) · Biochemical Pharmacology · doi:10.1016/j.bcp.2020.113910 · Cited by 55
Fang, Z., Chen, S., Pickford, P., Broichhagen, J., Hodson, D. J., Corrêa, I. R., Kumar, S., Görlitz, F., Dunsby, C., French, P. M. W., et al. (2020) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.0c00022 · Cited by 38
Tsai, M.-H. M., Chen, L., Baumann, M. H., Canals, M., Javitch, J. A., Lane, J. R., & Shi, L. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.3c00750 · Cited by 27
Self-citationAlhosan, N., Cavallo, D., Santiago, M., Kelly, E., & Henderson, G. (2025) · British Journal of Pharmacology · doi:10.1111/bph.17376 · Cited by 23
Parodi, B., Sanna, A., Cedola, A., Uccelli, A., & Kerlero de Rosbo, N. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.655212 · Cited by 20
Metadata
- Authors
- Klein Herenbrink, C., Sykes, D. A., Donthamsetti, P., Canals, M., Coudrat, T., Shonberg, J., Scammells, P. J., Capuano, B., Sexton, P. M., Charlton, S. J., Javitch, J. A., Christopoulos, A., & Lane, J. R.
- Venue
- Nature Communications
- Published
- 2016-02-24
- Volume
- 7
- Issue
- 1
- ISSN
- 2041-1723
- Cited by
- 328
- References
- 65
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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
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.
