Predictive modeling & mechanistic validation of synergistic pimodivir combinations for anti-influenza therapy via PB2cap affinity boost

  • NPJ Digit Med. 2025 Nov 21;8(1):712. doi: 10.1038/s41746-025-02083-2.
Peng Luo  #  1 Kexin Li  #  1 Yubin Xie  #  1 Kun Huang  #  1 Zhenzhi Qin  1 Jian-Piao Cai  1 Yu Fu  1 Jianli Cao  1 Sihang Cao  1 Ziyao Zhou  2 Zi-Wei Ye  3 Shuofeng Yuan  4  5  6  7
Affiliations
  • 1. Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
  • 2. College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.
  • 3. School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
  • 4. Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China. [email protected].
  • 5. State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China. [email protected].
  • 6. Centre for Virology, Vaccinology and Therapeutics, Hong Kong Science and Technology Park, Ma Liu Shui, Hong Kong Special Administrative Region, China. [email protected].
  • 7. Pandemic Research Alliance Unit at the University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China. [email protected].
  • # Contributed equally.
Abstract

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.

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