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The emergence of severe acute respiratory syndrome coronavirus (SARS-CoV-2) and its expansion to a worldwide pandemic resulted in efforts to assess and develop interventions to reduce the disease burden. Despite the introduction of vaccine programmes against SARS-CoV-2, global incidence levels in early 2022 remained high, demonstrating a need for the development of physiologically relevant models, which are essential for the identification of alternative antiviral strategies. The hamster model of SARS-CoV-2 infection has been widely adopted due to similarities with humans in terms of host cell entry mechanism (via ACE2), and aspects of symptomology and virus shedding. We have previously described a natural transmission hamster model that better represents the natural course of infection. In the present study, we have conducted further testing of the model using the first-in-class antiviral Neumifil, which has previously shown promise against SARS-CoV-2 after a direct intranasal challenge. Neumifil is an intranasally delivered carbohydrate-binding module (CBM) which reduces the binding of viruses to their cellular receptor. By targeting the host cell, Neumifil has the potential to provide broad protection against multiple pathogens and variants. This study demonstrates that using a combination of a prophylactic and therapeutic delivery of Neumifil significantly reduces the severity of clinical signs in animals infected via a natural route of transmission and indicates a reduction of viral loads in the upper respiratory tract. Further refinements of the model are required in order to ensure the adequate transmission of the virus. However, our results provide additional data to the evidence base of Neumifil efficacy against respiratory virus infection and demonstrate that the transmission model is a potentially valuable tool for testing antiviral compounds against SARS-CoV-2.

Use of a Preclinical Natural Transmission Model to Study Antiviral Effects of a Carbohydrate-Binding Module Therapy against SARS-CoV-2 in Hamsters