NB22: A Deep Learning-designed Peptide-siRNA Conjugate That Targets Lung Epithelial Cells Implicated in IPF

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Abstract Rationale: Idiopathic Pulmonary Fibrosis (IPF) is a progressive, restrictive pulmonary disease characterized by interstitial fibrosis. Approximately 40 thousand IPF cases & deaths are recorded each year in the United States, making IPF more rapidly lethal than many malignancies and as deadly as breast cancer. As the standard of care is supplemental oxygen and two FDA approved drugs that have adherence-limiting side effects, novel approaches are needed to treat the disease.Methods/Results: Using physics-aware deep learning models and single-cell transcriptomics, we designed a peptide-based ligand that targets lung epithelial cells implicated in IPF development. By conjugating the ligand to a chemically-stabilized siRNA payload, we demonstrate the resulting compound – NB22 – is capable of functional knockdown of a key inflammatory pathway in vivo. In vivo biodistribution profiling shows systemically-delivered NB22 preferentially localizes to the lung, with clearance via biliary and urine secretion. In a mouse model of systemic bleomycin-induced fibrosis, NB22 therapeutically reduces the inflammatory pathway to healthy baselines. In a rat model of intracheal bleomycin-induced fibrosis, NB22 prophylactically blunted disease progression and prevented mortality in a dose-dependent manner. Conclusion: Deep learning methods can be used to rapidly develop novel therapeutics for unmet clinical needs. NB22 is a peptide-siRNA conjugate currently in development to target a key inflammatory pathway implicated in IPF development

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