Community guidelines for GPCR ligand bias: IUPHAR review 32 - interview with Prof David Gloriam

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BJP Editor Dr Shivani Sachdev interviews Professor David Gloriam about the Community guidelines for GPCR ligand bias: IUPHAR review 32.
About the guidelines:
GPCRs modulate a plethora of physiological processes and mediate the effects of one-third of FDA-approved drugs. Depending on which ligand activates a receptor, it can engage different intracellular transducers. This ‘biased signalling’ paradigm requires that we now characterize physiological signalling not just by receptors but by ligand-receptor pairs. Ligands eliciting biased signalling may constitute better drugs with higher efficacy and fewer adverse effects. However, ligand bias is very complex, making reproducibility and description challenging. Here, we provide guidelines and terminology for any scientists to design and report ligand bias experiments. The guidelines will aid consistency and clarity, as the basic receptor research and drug discovery communities continue to advance our understanding and exploitation of ligand bias. Scientific insight, biosensors, and analytical methods are still evolving and should benefit from and contribute to the implementation of the guidelines, together improving translation from in vitro to disease-relevant in vivo models.
About Professor David Gloriam:
Professor Gloriam is a Professor of Peptides and Proteins at the Department of Drug Design and Pharmacology at the University of Copenhagen. His research spans database development, data science and experimental structural biology. In 2019, he became the leader of a Departmental Cluster for GPCR Function and Drug Discovery and in 2020 he joined the steering committee of a Faculty Center for Health Data Science. Professor Gloriam's research spans focuses on G protein-coupled receptors (GPCRs) - which mediate the effects of 1/3rd (ref) of drugs and 2/3rd of hormones (ref). His group develops computational and data driven methods for interdisciplinary receptor research and drug discovery, spanning:
- Biased signalling towards safer drugs and specific signalling probes
- Computational drug design
- Data science approaches to uncover receptor function: Discovery of physiological peptide hormones and determinants of receptor activation, effector G protein selectivity, genetic variants etc.
- Experimental structural biology
- Online research infrastructure