Clevudine for COVID-19

Background: Clevudine, an antiviral drug for chronic hepatitis B virus infection, is expected to inhibit the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Therefore, we conducted a prospective, single-blind, proof of concept clinical study to examine the antiviral efficacy and safety of clevudine compared to placebo in Korean corona virus disease 19 (COVID-19) patients with moderate severity. Methods: Adults with confirmed SARS-CoV-2 infection and symptom onset within 7 days were randomized 2:1 to 120 mg clevudine or placebo to receive one of treatments orally once-daily for 14 days. Antiviral efficacy outcomes were the proportion of patients with real-time reverse transcription polymerase chain reaction (RT-PCR) negative result for SARS-CoV-2 infection and cycle threshold (Ct) value changes from baseline. Clinical efficacy outcomes included proportion of patients who showed improvement in lung involvement by imaging tests, proportion of patients with normal body temperature, proportion of patients with normal oxygen saturation, and the changes in C-reactive protein (CRP) from baseline. Safety outcomes included changes in clinical laboratory tests, vital signs measurement, and physical examination from baseline, and incidence of adverse events. Results: The proportion of patients with real-time RT-PCR negative test and Ct value changes showed no significant difference between clevudine group and placebo group. The changes in Ct value from baseline were significantly greater in clevudine group compared to placebo group in patients with hypertension, and patients who underwent randomization during the first 5 and 7 days after the onset of symptoms. All clinical efficacy outcomes had no significant difference between clevudine group and placebo group. Clevudine was well tolerated and there was no significant difference in safety profile between two treatment groups. Conclusions: This is the first clinical study to compare the antiviral efficacy and safety of clevudine to placebo in Korean COVID-19 patients with moderate severity. The study has demonstrated a possible favorable outcome for the reduction of SARS-CoV-2 replication, with acceptable safety profile, when COVID-19 patients were treated with clevudine. Further large-scale clinical studies, preferably with various clinical endpoints and virus titer evaluation, are required to better understand the effectiveness of using clevudine in COVID-19 treatment. Considering recent trend in clinical development for antiviral drugs, we need to design a clinical study aiming for reducing clinical risk of COVID-19 in mild to moderate patients with at least one risk factor for serious illness.

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.

According to the World Health Organization (WHO), in the second half of 2022, there are about 606 million confirmed cases of COVID-19 and almost 6,500,000 deaths around the world. A pandemic was declared by the WHO in March 2020 when the new coronavirus spread around the world. The short time between the first cases in Wuhan and the declaration of a pandemic initiated the search for ways to stop the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or to attempt to cure the disease COVID-19. More than ever, research groups are developing vaccines, drugs, and immunobiological compounds, and they are even trying to repurpose drugs in an increasing number of clinical trials. There are great expectations regarding the vaccine’s effectiveness for the prevention of COVID-19. However, producing sufficient doses of vaccines for the entire population and SARS-CoV-2 variants are challenges for pharmaceutical industries. On the contrary, efforts have been made to create different vaccines with different approaches so that they can be used by the entire population. Here, we summarize about 8162 clinical trials, showing a greater number of drug clinical trials in Europe and the United States and less clinical trials in low-income countries. Promising results about the use of new drugs and drug repositioning, monoclonal antibodies, convalescent plasma, and mesenchymal stem cells to control viral infection/replication or the hyper-inflammatory response to the new coronavirus bring hope to treat the disease.