Selective estrogen receptor modulator, tamoxifen, inhibits Zika virus infection.
Scott F GradyAmelia K PintoMariah HassertJune A D'AngeloJames D BrienChristopher K ArnattPublished in: Journal of medical virology (2021)
Zika virus (ZIKV) is an arbovirus belonging to the flaviviridae family with a risk assessment that has been increasing in recent years and was labeled a global health emergency by the World Health Organization in 2016. There are currently no Food and Drug Administration-approved treatment options available for ZIKV, so expeditious development of treatment options is urgent. To expedite this process, an on-market drug, tamoxifen (TAM), was selected as a promising candidate for repurposing due to its wide range of biological activities and because it has already been shown to possess activity against hepatitis C virus, a flavivirus in a separate genus. Anti-ZIKV activity of TAM was assessed by compound screens using an infectious virus and mechanistic details were gleaned from time of addition and virucidal studies. TAM and an active metabolite, 4-hydroxytamoxifen (TAM-OH), both showed promising antiviral activity (EC50 ≈9 and 5 µM, respectively) in initial compound screening and up to 8-h postinfection, though the virucidal assay indicated that they do not possess any direct virucidal activity. Additionally, TAM was assessed for its activity against ZIKV in the human male germ cell line, SEM-1, due to the sexually transmitted nature of ZIKV owing to its extended survival times in germ cells. Virus titers show diminished replication of ZIKV over 7 days compared to controls. These data indicate that TAM has the potential to be repurposed as an anti-ZIKV therapeutic and warrants further investigation.
Keyphrases
- zika virus
- dengue virus
- estrogen receptor
- aedes aegypti
- hepatitis c virus
- risk assessment
- public health
- endothelial cells
- healthcare
- emergency department
- drug administration
- induced apoptosis
- gene expression
- human health
- human immunodeficiency virus
- high throughput
- genome wide
- climate change
- computed tomography
- oxidative stress
- machine learning
- single cell
- artificial intelligence
- positive breast cancer
- deep learning
- antiretroviral therapy
- drug discovery
- drug induced
- endoplasmic reticulum stress
- big data