Natural Products and Derivatives as Potential Zika virus Inhibitors: A Comprehensive Review.
Rosângela Santos PereiraFrançoise Camila Pereira SantosPriscilla Rodrigues Valadares CampanaVivian Vasconcelos CostaRodrigo Maia de PáduaDaniele G SouzaMauro Martins TexeiraFernão Castro BragaPublished in: Viruses (2023)
Zika virus (ZIKV) is an arbovirus whose infection in humans can lead to severe outcomes. This article reviews studies reporting the anti-ZIKV activity of natural products (NPs) and derivatives published from 1997 to 2022, which were carried out with NPs obtained from plants (82.4%) or semisynthetic/synthetic derivatives, fungi (3.1%), bacteria (7.6%), animals (1.2%) and marine organisms (1.9%) along with miscellaneous compounds (3.8%). Classes of NPs reported to present anti-ZIKV activity include polyphenols, triterpenes, alkaloids, and steroids, among others. The highest values of the selectivity index, the ratio between cytotoxicity and antiviral activity (SI = CC 50 /EC 50 ), were reported for epigallocatechin gallate (SI ≥ 25,000) and anisomycin (SI ≥ 11,900) obtained from Streptomyces bacteria, dolastane (SI = 1246) isolated from the marine seaweed Canistrocarpus cervicorni , and the flavonol myricetin (SI ≥ 862). NPs mostly act at the stages of viral adsorption and internalization in addition to presenting virucidal effect. The data demonstrate the potential of NPs for developing new anti-ZIKV agents and highlight the lack of studies addressing their molecular mechanisms of action and pre-clinical studies of efficacy and safety in animal models. To the best of our knowledge, none of the active compounds has been submitted to clinical studies.
Keyphrases
- zika virus
- aedes aegypti
- dengue virus
- room temperature
- oxide nanoparticles
- healthcare
- systematic review
- type diabetes
- emergency department
- randomized controlled trial
- skeletal muscle
- human health
- adipose tissue
- climate change
- multidrug resistant
- insulin resistance
- electronic health record
- gram negative
- big data
- weight loss
- data analysis