New anti-SARS-CoV-2 aminoadamantane compounds as antiviral candidates for the treatment of COVID-19.

Here, the antiviral activity of aminoadamantane derivatives were evaluated against SARS-CoV-2. The compounds exhibited low cytotoxicity to Vero, HEK293 and CALU-3 cells up to a concentration of 1,000 µM. The inhibitory concentration (IC50) of aminoadamantane was 39.71 µM in Vero CCL-81 cells and the derivatives showed significantly lower IC50 values, especially for compounds 3F4 (0.32 µM), 3F5 (0.44 µM) and 3E10 (1.28 µM). Additionally, derivatives 3F5 and 3E10 statistically reduced the fluorescence intensity of SARS-CoV-2 protein S from Vero cells at 10 µM. Transmission microscopy confirmed the antiviral activity of the compounds, which reduced cytopathic effects induced by the virus, such as vacuolization, cytoplasmic projections, and the presence of myelin figures derived from cellular activation in the face of infection. Additionally, it was possible to observe a reduction of viral particles adhered to the cell membrane and inside several viral factories, especially after treatment with 3F4. Moreover, although docking analysis showed favorable interactions in the catalytic site of Cathepsin L, the enzymatic activity of this enzyme was not inhibited significantly in vitro. The new derivatives displayed lower predicted toxicities than aminoadamantane, which was observed for either rat or mouse models. Lastly, in vivo antiviral assays of aminoadamantane derivatives in BALB/cJ mice after challenge with the mouse-adapted strain of SARS-CoV-2, corroborated the robust antiviral activity of 3F4 derivative, which was higher than aminoadamantane and its other derivatives. Therefore, aminoadamantane derivatives show potential broad-spectrum antiviral activity, which may contribute to COVID-19 treatment in the face of emerging and re-emerging SARS-CoV-2 variants of concern.

Protease inhibitors from Theobroma cacao impair SARS-CoV-2 replication in vitro.

SARS-CoV-2 is a newly emerging virus from the Coronaviridae family that has already infected over 700 million people worldwide and killed over 6 million. This virus uses protease molecules to replicate and infect the host, which makes these molecules targets for therapeutic substances to eliminate the virus and treat infected people. Through the protein-protein molecular docking approach, we detected two cystatins from Theobroma cacao, TcCYS3 and TcCYS4, described as papain-like protease inhibitors. These inhibitors decreased SARS-CoV-2 genomic copies without toxicity to Vero cells. There is a need to perform comprehensive studies in relevant animal models and to investigate the action mechanisms of protease inhibitors from Theobroma cacao that control the replication of SARS-CoV-2 in human cells.

New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning.

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.