tetano
Editor, Senior Moderator
Clin Microbiol Rev
. 2023 Jan 16;e0004022.
doi: 10.1128/cmr.00040-22. Online ahead of print.
Antiviral Approaches against Influenza Virus
Rashmi Kumari[SUP] 1 2 [/SUP], Suresh D Sharma[SUP] 1 [/SUP], Amrita Kumar[SUP] 1 [/SUP], Zachary Ende[SUP] 1 3 [/SUP], Margarita Mishina[SUP] 1 [/SUP], Yuanyuan Wang[SUP] 4 5 [/SUP], Zackary Falls[SUP] 6 [/SUP], Ram Samudrala[SUP] 6 [/SUP], Jan Pohl[SUP] 4 [/SUP], Paul R Knight[SUP] 2 [/SUP], Suryaprakash Sambhara[SUP] 1 [/SUP]
Affiliations
Abstract
Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact. Vaccines are the prophylaxis mainstay in the fight against influenza. However, vaccination fails to confer complete protection due to inadequate vaccination coverages, vaccine shortages, and mismatches with circulating strains. Antivirals represent an important prophylactic and therapeutic measure to reduce influenza-associated morbidity and mortality, particularly in high-risk populations. Here, we review current FDA-approved influenza antivirals with their mechanisms of action, and different viral- and host-directed influenza antiviral approaches, including immunomodulatory interventions in clinical development. Furthermore, we also illustrate the potential utility of machine learning in developing next-generation antivirals against influenza.
Keywords: antiviral agents; drug resistance mechanisms; influenza; machine learning; monoclonal antibodies.
. 2023 Jan 16;e0004022.
doi: 10.1128/cmr.00040-22. Online ahead of print.
Antiviral Approaches against Influenza Virus
Rashmi Kumari[SUP] 1 2 [/SUP], Suresh D Sharma[SUP] 1 [/SUP], Amrita Kumar[SUP] 1 [/SUP], Zachary Ende[SUP] 1 3 [/SUP], Margarita Mishina[SUP] 1 [/SUP], Yuanyuan Wang[SUP] 4 5 [/SUP], Zackary Falls[SUP] 6 [/SUP], Ram Samudrala[SUP] 6 [/SUP], Jan Pohl[SUP] 4 [/SUP], Paul R Knight[SUP] 2 [/SUP], Suryaprakash Sambhara[SUP] 1 [/SUP]
Affiliations
- PMID: 36645300
- DOI: 10.1128/cmr.00040-22
Abstract
Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact. Vaccines are the prophylaxis mainstay in the fight against influenza. However, vaccination fails to confer complete protection due to inadequate vaccination coverages, vaccine shortages, and mismatches with circulating strains. Antivirals represent an important prophylactic and therapeutic measure to reduce influenza-associated morbidity and mortality, particularly in high-risk populations. Here, we review current FDA-approved influenza antivirals with their mechanisms of action, and different viral- and host-directed influenza antiviral approaches, including immunomodulatory interventions in clinical development. Furthermore, we also illustrate the potential utility of machine learning in developing next-generation antivirals against influenza.
Keywords: antiviral agents; drug resistance mechanisms; influenza; machine learning; monoclonal antibodies.