Luca Zanon

Coronaviruses replicate their large RNA genomes through a conserved discontinuous transcription mechanism that generates the subgenomic RNAs required for viral assembly, making the process an attractive target for broad-spectrum antivirals. This poster presents IRBM's integrated screening platform, which pairs a phenotypic campaign on a stable SARS-CoV replicon cell line with an RNA-targeted approach directed at conserved stem-loop elements in the SARS-CoV-2 5'UTR. Mechanistic follow-up, surface plasmon resonance and NMR studies converge on chemically unrelated small molecules that preferentially interfere with subgenomic RNA synthesis and on validated RNA binders refined by structure-guided optimization. Download the poster to explore the screening cascade, mechanistic data and binding studies in detail.

Achieving sufficient unbound brain exposure while maintaining potent target inhibition is a major challenge in small-molecule drug discovery. This poster presents IRBM's integrated medicinal chemistry, pharmacology, and drug metabolism and pharmacokinetics strategy for an SHP2 inhibitor program. Compounds progress through a cascade that links CNS-relevant physicochemical design, complementary in vitro blood-brain barrier models, and in vivo rodent pharmacokinetics, using unbound brain exposure rather than potency alone to guide prioritization. This resource-efficient approach supported the selection of I-1000233, a brain-penetrant allosteric SHP2 inhibitor that achieves CNS exposure and efficacy in an orthotopic glioblastoma model. Download the poster to explore the optimization cascade, exposure data, and in vivo results in detail.

Peptides offer clear advantages as a therapeutic modality, but drug-like properties often require non-natural amino acids, whose computational handling remains a major challenge. This poster presents iPeptides, IRBM's in-house Python toolkit that operates directly on the molecular chemical graph, treating natural and non-natural residues with the same rigor. Its extensible design connects HELM sequence notation to atomistic 3D models and integrates combinatorial enumeration of imported monomer libraries, machine-learning-based prioritization, and downstream modeling, including cross-link and cycle-size optimization. The result is a unified framework for systematically exploring peptide chemical space across diverse design challenges. Download the poster to explore the workflow in detail.