Towards the discovery of potential RdRp inhibitors for the treatment of COVID-19: structure guided virtual screening, computational ADME and molecular dynamics study.

Coronavirus disease 2019 (COVID-19) has become a major challenge affecting almost every corner of the world, with more than five million deaths worldwide. Despite several efforts, no drug or vaccine has shown the potential to check the ever-mutating SARS-COV-2. The emergence of novel variants is a major concern increasing the need for the discovery of novel therapeutics for the management of this pandemic. Out of several potential drug targets such as S protein, human ACE2, TMPRSS2 (transmembrane protease serine 2), 3CLpro, RdRp, and PLpro (papain-like protease), RNA-dependent RNA polymerase (RdRP) is a vital enzyme for viral RNA replication in the mammalian host cell and is one of the legitimate targets for the development of therapeutics against this disease. In this study, we have performed structure-based virtual screening to identify potential hit compounds against RdRp using molecular docking of a commercially available small molecule library of structurally diverse and drug-like molecules. Since non-optimal ADME properties create hurdles in the clinical development of drugs, we performed detailed in silico ADMET prediction to facilitate the selection of compounds for further studies. The results from the ADMET study indicated that most of the hit compounds had optimal properties. Moreover, to explore the conformational dynamics of protein-ligand interaction, we have performed an atomistic molecular dynamics simulation which indicated a stable interaction throughout the simulation period. We believe that the current findings may assist in the discovery of drug candidates against SARS-CoV-2.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach.

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases.

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

Overview of Immune Response During SARS-CoV-2 Infection: Lessons From the Past.

After the 1918 flu pandemic, the world is again facing a similar situation. However, the advancement in medical science has made it possible to identify that the novel infectious agent is from the coronavirus family. Rapid genome sequencing by various groups helped in identifying the structure and function of the virus, its immunogenicity in diverse populations, and potential preventive measures. Coronavirus attacks the respiratory system, causing pneumonia and lymphopenia in infected individuals. Viral components like spike and nucleocapsid proteins trigger an immune response in the host to eliminate the virus. These viral antigens can be either recognized by the B cells or presented by MHC complexes to the T cells, resulting in antibody production, increased cytokine secretion, and cytolytic activity in the acute phase of infection. Genetic polymorphism in MHC enables it to present some of the T cell epitopes very well over the other MHC alleles. The association of MHC alleles and its downregulated expression has been correlated with disease severity against influenza and coronaviruses. Studies have reported that infected individuals can, after recovery, induce strong protective responses by generating a memory T-cell pool against SARS-CoV and MERS-CoV. These memory T cells were not persistent in the long term and, upon reactivation, caused local damage due to cross-reactivity. So far, the reports suggest that SARS-CoV-2, which is highly contagious, shows related symptoms in three different stages and develops an exhaustive T-cell pool at higher loads of viral infection. As there are no specific treatments available for this novel coronavirus, numerous small molecular drugs that are being used for the treatment of diseases like SARS, MERS, HIV, ebola, malaria, and tuberculosis are being given to COVID-19 patients, and clinical trials for many such drugs have already begun. A classical immunotherapy of convalescent plasma transfusion from recovered patients has also been initiated for the neutralization of viremia in terminally ill COVID-19 patients. Due to the limitations of plasma transfusion, researchers are now focusing on developing neutralizing antibodies against virus particles along with immuno-modulation of cytokines like IL-6, Type I interferons (IFNs), and TNF-α that could help in combating the infection. This review highlights the similarities of the coronaviruses that caused SARS and MERS to the novel SARS-CoV-2 in relation to their pathogenicity and immunogenicity and also focuses on various treatment strategies that could be employed for curing COVID-19.

Covid-19: current knowledge, disease potential, prevention and clinical advances.

The top priority of any nation is to lead the nation towards prosperity, progress, and economic growth, confronting several challenges and concerns arisen from global situations. The sudden outbreak of any disease defies the health care systems and economy of nations. COVID-19 is one of the viral diseases which broke out in Wuhan city of China in 2019. COVID-19 outbreak intermittently prevailed all over the world. It exposes the fragility of the established health care systems across the world in spite of comprising modern science and technology. Unfortunately, there is no chemotherapeutic agent in the regimen of antiviral drugs or no vaccine available to curb this infectious disease. As a consequence, this deadly infection has prevailed all over the world. The antiviral drugs used for viral diseases excluding COVID-19 infection are Ramdesvir, Favipiravir, and Ribavarin, and antimalarial agents (Chloroquine & Hydroxychloroquine) are being administered to the patients for redemption of this infection. Fortunately, these existing drugs have been found clinically active and are being used. In this review, we present the current scenario and status of epidemiology, diagnosis, treatment, vaccine development for COVID-19, and its impact on the socio-economic structure.