Back

Screening of MMV open-source libraries using Bunyamwera virus as a model reveals inhibitors of Oropouche virus infection

Cassani, N. M.; Pearson, H. M.; Barr, J. N.; Harris, M.; Jardim, A. C. G.

2026-01-14 microbiology
10.64898/2026.01.14.699453 bioRxiv
Show abstract

Arboviral infections remain a major public health concern in tropical and subtropical regions, where environmental and socioeconomic conditions facilitate the circulation of diverse RNA viruses, including over 350 members of the recently renamed Peribunyaviridae family. In this regard, Oropouche virus (OROV) has caused explosive outbreaks in the Amazon region and shows wider distribution in Brazil, with confirmed neurological infections and fatalities in 2024. The absence of effective antiviral therapies highlights an urgent need for the discovery of anti-OROV drugs. In this work we evaluated the antiviral potential of the Pandemic Response Box and Global Health Priority Box libraries from Medicines for Malaria Venture (MMV) against OROV, using a well-established Bunyamwera virus (BUNV) system, widely used as a prototype of the Peribunyaviridae family, expressing the reporter gene eGFP. A screening protocol based on fluorescent reporter gene detection by live cell imaging was employed. 9 compounds were identified with significant activity against BUNV-eGFP with 3 being highlighted as the most promising hits: Trimetrexate, GSK-983 and MMV1634385. These compounds were validated by testing for activity against OROV, and in these assays, GSK-983 exhibited the lowest EC50 value and consequently the highest selectivity index. Future studies should evaluate the antiviral efficacy of GSK-983 and assess its pharmacokinetic profile. Furthermore, our results indicate that our approach employing BUNV-eGFP as a model against OROV can lead to the discovery of promising and interesting hit compounds, thereby providing a validated screening approach for future antivirals against Oropouche fever.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.