Praziquantel for COVID-19

Objectives Essential oils and their chemical constituents are recognized for their antimicrobial properties against many infections. In this study, some molecules of essential oils are identified as potentially effective on the MPro of SARS-CoV-2 and as a BRD2 inhibitors. These essential oils were combined in an optimum formulation as (Anti-COV19-VERS4). This study evaluates the role of Anti-COV19-VERS4, in preventing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) contagiousness and its secondary effects on infected tissues by in-vivo experiments. Methods Anti-COV19-VERS4 is formulated based on molecular docking studies. Following cytotoxic and antiviral dose determination, SARS-CoV-2 infected transgenic mice were treated with Anti-COV19-VERS4 spray. RT-qPCR was used to detect viral load and histopathologic analysis was performed. The experiment was terminated on the days of 5 and 6 when SARS-CoV-2 infection peaked. RT-qPCR was used to detect viral load and histopathologic analysis was performed. Two-way ANOVA and Student’s t -tests were used for statistical analysis. Results Cytotoxicity and neutralization assays revealed that the most effective dilution of Anti-COV19-VERS4 showing cytopathic effect (CPE) was 1/2600 and the 24-hour CC50 dose was 0.0154 mg/L. During in-vivo trials, 3 out of 14 mice in the control group died due to infection. The virus load of the Anti-COV19-VERS4 spray group on day 6 decreased compared to the control group. Moreover, lung injury scores for the Anti-COV19-VERS4 spray group were significantly lower than the control group on the 6 th day (p<0.05). This study demonstrates that Anti-COV19-VERS4 may be useful for preventing the contagiousness of SARS-CoV-2 and relieving lung injury. Conclusion Anti-COV19-VERS4 may be helpful as a cost-effective and efficacious adjunctive therapy for SARS-CoV-2.

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.