Celtarys – GPCR Profiling: Techniques and Applications

GPCR profiling techniques are diverse and constantly improving. With advancements in functional assays, hit identification and target validation, GPCR profiling is clearly an indisputably vital tool for drug discovery.

Being fundamental to many cellular processes and correct biological functioning, G-protein receptors are essential for human health and disease treatment. They, therefore, remain the largest family of therapeutic targets for drug discovery. GPCR agonistic and antagonistic drugs have provided therapeutic treatment for various diseases such as asthma, hypertension, and chronic pain 1.

GPCR profiling is key to discovering GPCR-based ligands which may represent a druggable target for therapeutic purposes. GPCR profiling is a major focus of pharmaceutical research, and there have been many recent advancements in compound screening. Yet, there are still challenges to identifying and validating target ligands.

Compound Screening in GPCR profiling

The first step in GPCR profiling is compound screening. Affinity binding assays are typically used to determine the strength of binding of a ligand (protein, peptide, or small molecule drug) to a target biomolecule. Binding assays rely on the quantitative/qualitative detection of binding partners as well as the resulting binding complexes. For this purpose, either the target biomolecule, ligands or both need to be labeled. The second step is to measure the GPCR activity, through a variety of ligand-binding functional assays. Receptor ligand-binding assays, such as CELT-419 , describes the interaction between a receptor and its ligands providing information about association/dissociation rates and the density of the receptor within biological samples 2. However, the limited availability of labeled ligands restricts this assay’s application for profiling orphans targets. 

These functional assays, can be G-protein dependent or independent and rely on high-throughput screening for hit identification. These identify potential GPCR ligands via quantifying the increase or decrease in cellular secondary messengers, directly or indirectly. Direct measurement of cellular secondary messengers involves directly detecting the levels of the messengers themselves, while indirect measurement involves detecting downstream effects of the messengers. On the other side, label-free, whole-cell assays measure signal transduction within the cell, with integrative/cumulative responses and use a biosensor to convert these ligand-induced changes into quantifiable signals 2.

Another important aspect to take in consideration is the impact of GPCR structures and their dimerization on the target-ligand pharmacology. Compounds that affect this process could be potential drug candidates, therefore assessing the effects of these compounds is beneficial to GPCR profiling. However, identifying a specific signal produced by the direct interaction between these cell membrane proteins is tricky, as responses within the cell will dilute these signals.

Developments in computational methods enable in silico screening of GPCR ligands for GCPR profiling. This method displays an increased probability of hit identification of new druggable ligands. Unfortunately, the limited information of the GPCR’s 3D strucutre is a drawback to this method, but recent advances in this area is combatting this 2.

Further GPCR Profiling

Historically GPCR profiling for drug discovery only targeted typical orthosteric GPCR ligands. New ligand classes that bind to orthosteric and allosteric sites are favoured to increase the discovery of potential candidates. Advances in GPCR profiling are required to optimise pharmacological screening assays that detect compounds with suitable receptor activity 3.

The combination of compound screening to assess the target-based structure affinity relationship (SAR) and structural liability relationship (SLR), along with pharmacological dossier assays has significantly improved target validation in GPCR screening. This combination can completely characterize the pharmaceutical properties of a ligand against a particular GPCR after hit identification 3.

High-throughput parallel screening of mutants against wild types is another recent tool in GPCR profiling4. This screening determines GPCR SAR structures and enhances the accuracy of receptor models and SAR pharmacological profiles of biological targets. This GPCR profiling technique will advance further with more evaluation and refinement of GPCR 3-D models, expanding insights into GPCR ligand binding and function 3.

Biased signaling GPCR profiling uses IT visualization to collectively obtain information on signaling bias. This allows for more readily categorization of compounds with the most relevant profile for drug discovery. Quantitative imagery and recently developed label-free biosensors have been utilized to for successful hit identification and target validation of GPCR ligands. This method measures real-time cellular responses to ligands or drug-like compounds, measuring changes in cell adherence, cell shape, cell size and volume, and interactions between cells to quantify the cellular response resulting from the activation by a GPCR ligand 35.

Here we scratch only the surface of GPCR profiling techniques for hit identification and target validation of GPCRs, these various methods, which are constantly advancing, are crucial to understanding GPCR profiles for drug discoveries. Future developments in our understanding of GPCR 3-D structures and increases in the availability of GPCR crystals and fluorescent ligands will greatly contribute to GPCR profiling and drug discovery.


  1. Wise, A., Gearing, K. and Rees, S. 2002. Target validation of G-protein coupled receptors. Drug discovery today. 7(4), pp.235-246.
  2. Zhang, R. and Xie, X. 2012. Tools for GPCR drug discovery. Acta Pharmacologica Sinica. 33(3), pp.372-384.
  3. Cvijic, M.E., Sum, C.S., Alt, A. and Zhang, L. 2015. GPCR profiling: from hits to leads and from genotype to phenotype. Drug Discovery Today: Technologies. 18, pp.30-37.
  4. Peng C, Wang H, Xu X, Wang X, Chen X, Wei W, Lai Y, Liu G, Godwin ID, Li J, Zhang L, Xu J. High-throughput detection and screening of plants modified by gene editing using quantitative real-time polymerase chain reaction. Plant J. 2018 Aug;95(3):557-567. doi: 10.1111/tpj.13961. Epub 2018 Jun 15. PMID: 29761864.
  5. Luttrell LM, Maudsley S, Bohn LM. Fulfilling the Promise of "Biased" G Protein-Coupled Receptor Agonism. Mol Pharmacol. 2015 Sep;88(3):579-88. doi: 10.1124/mol.115.099630. Epub 2015 Jul 1. PMID: 26134495; PMCID: PMC4551052.