Evidence map›Paper›PMID 25506327›Full record

ReviewFrontiers in pharmacology2014

Discovery of GPCR ligands for probing signal transduction pathways.

Simone Brogi, Andrea Tafi, Laurent Désaubry, Canan G Nebigil

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 36 citations in OpenAlex.

  1. Do GPCRs constitute the target of 30% of newly approved drugs in Germany?Naunyn-Schmiedeberg's archives of pharmacology · 2026
    Article
  2. Review
  3. Specificities of Protein Homology Modeling for Allosteric Drug Design.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  4. Potent and Subtype-Selective Dopamine DJournal of medicinal chemistry · 2021
    Article
  5. Trends in application of advancing computational approaches in GPCR ligand discovery.Experimental biology and medicine (Maywood, N.J.) · 2021
    Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design.Advances in protein chemistry and structural biology · 2018
    Review
  12. Article
  13. Review
  14. Immunomodulatory Effects Mediated by Dopamine.Journal of immunology research · 2016
    Review
  15. Review
  16. Review
  17. New paradigms in GPCR drug discovery.Biochemical pharmacology · 2015
    Review
  18. Applications of phototransformable fluorescent proteins for tracking the dynamics of cellular components.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2015
    Review
  19. Activators of G protein signaling in the kidney.The Journal of pharmacology and experimental therapeutics · 2015
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Simone BrogiEuropean Research Centre for Drug Discovery and Development (NatSynDrugs), University of Siena Siena, Italy ; Department of Biotechnology, Chemistry and Pharmacy, University of Siena Siena, Italy.
Andrea TafiDepartment of Biotechnology, Chemistry and Pharmacy, University of Siena Siena, Italy.
Laurent DésaubryTherapeutic Innovation Laboratory, UMR7200, CNRS/University of Strasbourg Illkirch, France.
Canan G NebigilReceptor Signaling and Therapeutic Innovations, GPCR and Cardiovascular and Metabolic Regulations, Biotechnology and Cell Signaling Laboratory, UMR 7242, CNRS/University of Strasbourg - LabEx Medalis Illkirch, France.
University of Siena · ITLaboratoire d'Innovation Thérapeutique · FRUniversité de Strasbourg · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure-selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

Indexed as

allosteric modulatorsbiased agonistsbiased signalingbivalent ligandsGPCRG protein-coupled receptorshigh throughput dockinghomology modeling

Identifiers

PMID25506327
PMCPMC4246677
OpenAlexW2080365079

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.