Predictive modeling & mechanistic validation of synergistic pimodivir combinations for anti-influenza therapy via PB2cap affinity boost
This study introduces a machine learning framework to predict effective antiviral combinations for influenza A. It identifies Pimodivir with Epinephrine or L-Adrenaline as synergistic agents, confirmed by experiments demonstrating increased binding affinity and viral suppression. Multiple synergy scoring methods validate these drug combinations' potential, offering a strategic pathway for designing rational combination therapies against influenza and other RNA viruses.
Authors
- Yu Fu (ORCID: https://orcid.org/0000-0001-8878-3131)
- Zhenzhi Qin
- Yubin Xie
- Ziyao Zhou (ORCID: https://orcid.org/0000-0002-5484-5442)
- Peng Luo (ORCID: https://orcid.org/0000-0002-8215-2045)
- Jianli Cao (ORCID: https://orcid.org/0000-0003-1464-7377)
- Jian‐Piao Cai (ORCID: https://orcid.org/0000-0003-4077-3852)
- Zi‐Wei Ye (ORCID: https://orcid.org/0000-0002-6446-4299)
- Kexin Li (ORCID: https://orcid.org/0009-0001-7193-0534)
- Kun Huang (ORCID: https://orcid.org/0000-0002-3347-0688)
- Sijia Cao (ORCID: https://orcid.org/0000-0002-4545-2997)
- Shuofeng Yuan
Institutions
- Hong Kong Science and Technology Parks Corporation (HK)
- Sichuan Agricultural University (CN)
- HKU-Pasteur Research Pole (HK)
- University of Hong Kong (HK)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1038/s41746-025-02083-2
- Citations
- 1
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Natural Science Foundation of China
- Innovation and Technology Commission