The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Operational models of pharmacological agonism
Abstract
The traditional receptor-stimulus model of agonism began with a description of drug action based on the law of mass action and has developed by a series of modifications, each accounting for new experimental evidence. By contrast, in this paper an approach to modelling agonism is taken that begins with the observation that experimental agonist-concentration effect, E/[A], curves are commonly hyperbolic and develops using the deduction that the relation between occupancy and effect must be hyperbolic if the law of mass action applies at the agonist-receptor level. The result is a general model that explicity describes agonism by three parameters: an agonist-receptor dissociation constant, KA; the total receptor concentration, [R0]; and a parameter, KE, defining the transduction of agonist-receptor complex, AR, into pharmacological effect. The ratio, [R0]/KE, described here as the ‘transducer ratio’, τ, is a logical definition for the efficacy of an agonist in a system. The model may be extended to account for non-hyperbolic E/[A] curves with no loss of meaning. Analysis shows that an explicit formulation of the traditional receptor-stimulus model is one particular form of the general model but that it is not the simplest. An alternative model is proposed, representing the cognitive and transducer functions of a receptor, that describes agonist action with one fewer parameter than the traditional model. In addition, this model provides a chemical definition of intrinsic efficacy making this parameter experimentally accessible in principle. The alternative models are compared and contrasted with regard to their practical and conceptual utilities in experimental pharmacology.
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
SOME QUANTITATIVE USES OF DRUG ANTAGONISTS
ARUNLAKSHANA, O., & Schild, H. O. (1959) · British Journal of Pharmacology and Chemotherapy · doi:10.1111/j.1476-5381.1959.tb00928.x · Cited by 3,933
Decreased Catecholamine Sensitivity and β-Adrenergic-Receptor Density in Failing Human Hearts
Bristow, M. R., Ginsburg, R., Minobe, W., Cubicciotti, R. S., Sageman, W. S., Lurie, K., Billingham, M. E., Harrison, D. C., & Stinson, E. B. (1982) · New England Journal of Medicine · doi:10.1056/nejm198207223070401 · Cited by 2,270
The role of hormone receptors and GTP-regulatory proteins in membrane transduction
Rodbell, M. (1980) · Nature · doi:10.1038/284017a0 · Cited by 1,749
De Lean, A., Stadel, J. M., & Lefkowitz, R. J. (1980) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(20)79672-9 · Cited by 1,612
A MODIFICATION OF RECEPTOR THEORY
STEPHENSON, R. P. (1956) · British Journal of Pharmacology and Chemotherapy · doi:10.1111/j.1476-5381.1956.tb00006.x · Cited by 1,156
Furchgott, R. F. (1972) · Springer eBooks · doi:10.1007/978-3-642-65249-3_9 · Cited by 814
Relaxation and fluctuations of membrane currents that flow through drug-operated channels
Colquhoun, D., & Hawkes, A. G. (1977) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1977.0137 · Cited by 622
KENT, R. S., DE LEAN, A., & LEFKOWITZ, R. J. (1980) · Molecular Pharmacology · doi:10.1016/s0026-895x(25)14062-5 · Cited by 553
Furchgott, R. F., & Bursztyn, P. (1967) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1967.tb53817.x · Cited by 522
Interaction at end-plate receptors between different choline derivatives
del Castillo, J., & Katz, B. (1957) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1957.0018 · Cited by 475
Research works printed but not linked: 12
- Ariens E. J., 1954, Affinity and intrinsic activity in the theory of competitive inhibition. Archs int, Pharmacodyn. Ther., 99, 32
- Ariens E. J., 1956, A theoretical basis of molecular pharmacology, part III, Arzneimittel-Forsch., 6, 737
- Clark A. J. 1937 In Handbuch der experimentell Pharmakologie (ed. W. Heubner &J. Schuller). Berlin: Julius Springer.
- Dean D. M., 1978, Contribution of adrenergic and " purinergic, J. Pharmacol, exp. Ther., 207, 431
- Fuder H., 1981, Alpha adrenoceptors of rabbit aorta and stomach fundus. J. Pharmacol, exp, Ther., 217, 1
- Furchgott R. F., 1966, The use of p-haloalkylamines in the differentiation of receptors and in the determination of dissociation constants of receptor-agonist complexes, Adv. Drug Res., 3, 21
- Gaddum J. H., 1937, The quantitative effects of antagonistic drugs, J. Physiol. Lond., 89, 7
- Gaddum J. H., 1953, Biossays and mathematics, Pharmac. Rev., 5, 87
- Mackay D., 1966, a A new method for the analysis of drug-receptor interactions, Adv. Drug Res., 3, 1
- Stephenson R. P., 1953, Acetylcholine receptors and alkyltrimethylammonium salts, Abstr. Comm. X IX International Physiol. Congress. Montreal, 801
- Triggle D. J., 1980, Desensitization, T. I. P., 395
- Waud D. R., 1969, A quantitative model for the effect of a saturable uptake on the slope of the dose-response curve. J. Pharmacol, exp, Ther., 167, 140
Cited by
Allosteric modulators of GPCRs: a novel approach for the treatment of CNS disorders
Jeffrey Conn, P., Christopoulos, A., & Lindsley, C. W. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2760 · Cited by 1,063
Regulation of μ-opioid receptors: desensitization, phosphorylation, internalization, and tolerance
Williams, J. T., Ingram, S. L., Henderson, G., Chavkin, C., von Zastrow, M., Schulz, S., Koch, T., Evans, C. J., & Christie, M. J. (2013) · Pharmacological Reviews · doi:10.1124/pr.112.005942 · Cited by 802
General Pharmacokinetic Model for Drugs Exhibiting Target-Mediated Drug Disposition
Mager, D. E., & Jusko, W. J. (2001) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1023/a:1014414520282 · Cited by 605
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
“Using these data, we normalized their potencies and efficacies with respect to the efficiency of DAMGO in each assay using the classic operational model to determine bias factors (Black and Leff, 1983; 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
Emerging paradigms in GPCR allostery: implications for drug discovery
Wootten, D., Christopoulos, A., & Sexton, P. M. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4052 · Cited by 498
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
The role of kinetic context in apparent biased agonism at GPCRs
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., et al. (2016) · Nature Communications · doi:10.1038/ncomms10842 · Cited by 328
Structure-based discovery of nonhallucinogenic psychedelic analogs
Cao, D., Yu, J., Wang, H., Luo, Z., Liu, X., He, L., Qi, J., Fan, L., Tang, L., Chen, Z., et al. (2022) · Science · doi:10.1126/science.abl8615 · Cited by 312
Cell viability assessment using the Alamar blue assay: A comparison of 2D and 3D cell culture models
Bonnier, F., Keating, M. E., Wróbel, T. P., Majzner, K., Baranska, M., Garcia-Munoz, A., Blanco, A., & Byrne, H. J. (2015) · Toxicology in Vitro · doi:10.1016/j.tiv.2014.09.014 · Cited by 276
Community 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
Greenwald, M. K., Comer, S. D., & Fiellin, D. A. (2014) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2014.07.035 · Cited by 174
Vandeputte, M. M., Van Uytfanghe, K., Layle, N. K., St. Germaine, D. M., Iula, D. M., & Stove, C. P. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00064 · Cited by 161
Weston, C., Winfield, I., Harris, M., Hodgson, R., Shah, A., Dowell, S. J., Mobarec, J. C., Woodlock, D. A., Reynolds, C. A., Poyner, D. R., et al. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.751362 · Cited by 95
Optimization of a series of mu opioid receptor (MOR) agonists with high G protein signaling bias
Kennedy, N. M., Schmid, C. L., Ross, N. C., Lovell, K. M., Yue, Z., Chen, Y. T., Cameron, M. D., Bohn, L. M., & Bannister, T. D. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01136 · Cited by 71
Karamitri, A., Plouffe, B., Bonnefond, A., Chen, M., Gallion, J., Guillaume, J.-L., Hegron, A., Boissel, M., Canouil, M., Langenberg, C., et al. (2018) · Science Signaling · doi:10.1126/scisignal.aan6622 · Cited by 62
Low 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
Parameter Identifiability of Fundamental Pharmacodynamic Models
Janzén, D. L. I., Bergenholm, L., Jirstrand, M., Parkinson, J., Yates, J., Evans, N. D., & Chappell, M. J. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00590 · Cited by 44
Vuckovic, Z., Wang, J., Pham, V., Mobbs, J. I., Belousoff, M. J., Bhattarai, A., Burger, W. A., Thompson, G., Yeasmin, M., Nawaratne, V., et al. (2023) · eLife · doi:10.7554/elife.83477 · Cited by 34
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
On astrocyte-neuron interactions: broad insights from the striatum
Khakh, B. S. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.009 · Cited by 23
Alhosan, N., Cavallo, D., Santiago, M., Kelly, E., & Henderson, G. (2025) · British Journal of Pharmacology · doi:10.1111/bph.17376 · Cited by 23
Xie, B., Le Rouzic, V. P., Goldberg, A., Tsai, M.-H. M., Chen, L., Zhang, T., Sinha, A., Pan, Y.-X., Baumann, M. H., & Shi, L. (2023) · Neuropharmacology · doi:10.1016/j.neuropharm.2023.109442 · Cited by 18
Vandeputte, M. M., Bilel, S., Tirri, M., Corli, G., Bassi, M., Layle, N. K., Fantinati, A., Walther, D., Iula, D. M., Baumann, M. H., et al. (2024) · Neuropharmacology · doi:10.1016/j.neuropharm.2024.110113 · Cited by 19
Metadata
- Authors
- Black, J. W., & Leff, P.
- Venue
- Proceedings of the Royal Society of London. Series B. Biological Sciences
- Publisher
- The Royal Society
- Published
- 1983-12-22
- Volume
- 220
- Issue
- 1219
- Pages
- 141-162
- ISSN
- 0080-4649
- 2053-9193
- Cited by
- 951
- References
- 51
- Language
- en
- Licence
- royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)
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.