Antiviral Approaches against Influenza Virus

Area of Science:

• Virology
• Immunology
• Public Health

Background:

• Influenza virus infections pose a significant global health burden, causing seasonal epidemics and pandemics with high morbidity, mortality, and economic impact.
• Current influenza vaccines face limitations including incomplete protection, inadequate coverage, shortages, and strain mismatches, necessitating alternative control measures.
• Antiviral drugs are essential for prophylaxis and treatment, especially in high-risk groups, to mitigate influenza-associated illness and death.

Purpose of the Study:

• To review currently FDA-approved influenza antivirals and their mechanisms of action.
• To explore diverse antiviral strategies, including viral-directed, host-directed, and immunomodulatory interventions in clinical development.
• To highlight the potential of machine learning in the development of next-generation influenza antivirals.

Main Methods:

• Comprehensive literature review of FDA-approved influenza antivirals.
• Analysis of various antiviral approaches targeting viral replication and host responses.
• Exploration of emerging strategies and computational methods like machine learning.

Main Results:

• Summary of established influenza antiviral drugs and their modes of action.
• Overview of investigational antiviral therapies and immunomodulatory interventions.
• Identification of machine learning as a promising tool for novel antiviral discovery.

Conclusions:

• Existing antivirals are vital but require complementary strategies for comprehensive influenza control.
• Novel viral- and host-directed therapies, alongside immunomodulation, offer potential advancements.
• Machine learning presents a transformative opportunity for developing more effective next-generation influenza antivirals to address global health challenges.