P0028 Discovery of GLP-2R agonists using computational molecular design techniques

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Abstract Background Computational drug discovery is a powerful, cost-effective approach for accelerating drug development, providing key insights into modulating the GLP-2 receptor (GLP-2R), an important target for gastrointestinal diseases like short bowel syndrome1. Methods This study uses diverse in silico molecular design methods, mainly virtual screening2-3, to discover nonpeptidic small-molecule GLP-2R agonists that overcome the drawbacks of peptide-based treatments. The first strategy identifies virtual high-affinity binders on the contact surface between GLP-2 and GLP-2R, which may stabilize the GLP-2R/GLP-2 complex forming hydrogen bonds with both partners to enhance receptor activation. A second strategy uses fragment-based virtual screening to find critical fragments that bind strongly to GLP-2R residues essential for activation. The goal of using these fragments to screen various hit-like and lead-like molecule libraries is to discover small molecules with strong activation potential. Given the difficulty of targeting the large, shallow orthosteric site of GLP-2R with small molecules, a third computational approach focuses on finding small moleculesthat bind to allosteric sites, identified through computational modeling. Results In silico studies have led to the identification of a number of potential hit compounds that will undergo experimental testing to evaluate their effectiveness in activating GLP-2R. Conclusion This study lays the foundation for further lead optimization, where computational approaches will help find new drug candidates, that may advance specialized therapies for gastrointestinal disorders with limited treatment options. Fundings 2.1 "Rafforzamento e potenziamento della ricerca biomedica del SSN", finanziato dall’Unione europea – NextGenerationEU, CUP C53C22001140007. 2022 PNRR Project"Changing the future of intestinal failure in intestinal chronic inflammation: towards innovative predictive factors and therapeutic targets" code: PNRR-MAD-2022-12376791.