Nanotechnology. 2023 Jan 23. doi: 10.1088/1361-6528/acb558. Online ahead of print.
ABSTRACT
The search for new strategies to curb the spread of the SARS-CoV-2 coronavirus, which causes COVID-19, has become a global priority. Various nanomaterials have been proposed as ideal candidates to inactivate the virus; however, because of the high level of biosecurity required for their use, alternative models should be determined. This study aimed to compare the effects of two types of nanomaterials-gold (AuNPs) and silver nanoparticles (AgNPs), recognized for their antiviral activity and affinity with the coronavirus spike protein-using PhiX174 and enveloped Phi6 bacteriophages as models. To reduce the nanoparticles’ toxicity, a species known for its intermediate antiviral activity, Solanum mammosum L. (Sm), was used. NPs prepared with sodium borohydride (NaBH4) functioned as the control. Antiviral activity against PhiX174 and Phi6 was analyzed using its seed, fruit, leaves, and essential oil; the leaves were the most effective on Phi6. Using the aqueous extract of the leaves, AuNPs-Sm of 5.34 ± 2.25 nm and AgNPs-Sm of 15.92 ± 8.03 nm, measured by transmission electron microscopy (TEM), were obtained. When comparing NPs with precursors, both gold(III) acetate and silver nitrate were more toxic than their respective NPs (99.99% at 1 mg/mL). The AuNPs-Sm were less toxic, reaching 99.30% viral inactivation at 1 mg/mL, unlike the AgNPs-Sm, which reached 99.94% at 0.01 mg/mL. Gallic acid was the main component identified in the leaf extract using high performance liquid chromatography with diode array detection (HPLC-DAD). The FT-IR spectra showed the presence of a large proportion of polyphenolic compounds, and the antioxidant analysis confirmed the antiradical activity. The control NPs showed less antiviral activity than the AuNPs-Sm and AgNPs-Sm, which was statistically significant; this demonstrates that both the S. mammosum extract and its corresponding NPs have a greater antiviral effect on the surrogate Phi bacteriophage, which is an appropriate model for studying SARS-CoV-2.
PMID:36689773 | DOI:10.1088/1361-6528/acb558
