An extraordinary opportunity to generate proprietary chemo-informatics for GPCR drug discovery

G protein-coupled receptors (GPCRs) are important drug targets because they are involved in a variety of physiological processes, including regulation of neurotransmitter release, hormone secretion, and immune cell function. In recent decades, remarkable progress has been made in the discovery of GPCR drugs, and more than 30% of FDA-approved drugs target these receptors. However, only about 10% of GPCRs have been tapped as targets, leaving huge potential for future drug development.

The universe of data for experimentally validated GPCR ligands and modulators is sparse

GPCRdb is the comprehensive public data source and a valuable resource for researchers accessing data on GPCRs. To address variability in data quality and availability, GPCRdb has implemented a data curation process in which experts review and comment on the data to ensure its accuracy and completeness. This process has helped standardize the data and improve their quality. Nevertheless, the data are generated in a wide range of experimental setups, and the approximately 4.7x10^5 ligands with bioactivity are distributed among only a select number of GPCRs. Some GPCRs, such as the β2-adrenergic receptor, have been extensively studied and have a large body of literature. In contrast, some GPCRs have limited research and for more than 50% of all GPCRs no ligand is known to date. Hence, the universe of data for experimentally validated GPCR ligands and modulators is therefore sparse.

Creating a one-of-a-kind database that identifies hits under highly standardized experimental conditions

Multiplexing cellular drug-function assays offers a distinct advantage for identifying GPCR ligands with bioactivity across all non-olfactory GPCRs. This approach allows for the simultaneous testing of tens of thousands of ligands on hundreds of GPCRs, providing robust quantitative data on receptor activity and selectivity. Imagine being able to create a standardized dataset of 100,000 compounds of different chemotypes for more than 300 GPCR targets within a year and improve the data depth by at least 100-fold. Or screening of a similar sized peptide or nanobody library in this setup.

Integrating that rich data source with public data and structural receptor data will create unique information that will allow unprecedented ML models for GPCR drug discovery. This is what we are up to at Design Pharma. We are creating boundless opportunities for therapeutics based on the use of broad and ultra-deep functional GPCR screening.

Follow us on LinkedIn to learn more about new technologies that are transforming GPCR drug discovery.