Developmental Landscape of Potential Vaccine Candidates Based on Viral Vector for Prophylaxis of COVID-19.

Severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, arose at the end of 2019 as a zoonotic virus, which is the causative agent of the novel coronavirus outbreak COVID-19. Without any clear indications of abatement, the disease has become a major healthcare threat across the globe, owing to prolonged incubation period, high prevalence, and absence of existing drugs or vaccines. Development of COVID-19 vaccine is being considered as the most efficient strategy to curtail the ongoing pandemic. Following publication of genetic sequence of SARS-CoV-2, globally extensive research and development work has been in progress to develop a vaccine against the disease. The use of genetic engineering, recombinant technologies, and other computational tools has led to the expansion of several promising vaccine candidates. The range of technology platforms being evaluated, including virus-like particles, peptides, nucleic acid (DNA and RNA), recombinant proteins, inactivated virus, live attenuated viruses, and viral vectors (replicating and non-replicating) approaches, are striking features of the vaccine development strategies. Viral vectors, the next-generation vaccine platforms, provide a convenient method for delivering vaccine antigens into the host cell to induce antigenic proteins which can be tailored to arouse an assortment of immune responses, as evident from the success of smallpox vaccine and Ervebo vaccine against Ebola virus. As per the World Health Organization, till January 22, 2021, 14 viral vector vaccine candidates are under clinical development including 10 nonreplicating and four replicating types. Moreover, another 39 candidates based on viral vector platform are under preclinical evaluation. This review will outline the current developmental landscape and discuss issues that remain critical to the success or failure of viral vector vaccine candidates against COVID-19.

Identification of novel human renin inhibitors through a combined approach of pharmacophore modelling, molecular DFT analysis and in silico screening.

Renin is an aspartyl protease of the renin-angiotensin system (RAS) and the first enzyme of the biochemical pathway for the generation of angiotensin II - a potent vasoconstrictor involved in the maintenance of cardiovascular homeostasis and the regulation of blood pressure. High enzymatic specificity of renin and its involvement in the catalysis of the rate-limiting step of the RAS hormone system qualify it as a good target for inhibition of hypertension and other associated diseases. Ligand-based pharmacophore model (Hypo1) was generated from a training set of 24 compounds with renin inhibitory activity. The best hypothesis consisted of one Hydrogen Bond Acceptor (HBA), three Hydrophobic Aliphatic (HY-Al) and one Ring Aromatic (AR) features. This well-validated pharmacophore hypothesis (correlation coefficient 0.95) was further utilized as a 3D query to screen database compounds, which included structures from two natural product repositories. These screened compounds were further analyzed for drug-likeness and ADMET studies. The compounds which satisfied the qualifying criteria were then subjected to molecular docking and Density Functional Theory (DFT) analysis in order to discern their atomic level interactions at the active site of the 3D structure of rennin. The pharmacophore-based modelling that has been used to generate the novel findings of the present study would be an avant-garde approach towards the development of potent inhibitors of renin.

Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics.

Plant-derived natural products (NPs) play a vital role in the discovery of new drug molecules and these are used for development of novel therapeutic drugs for a specific disease target. Literature review suggests that natural products possess strong inhibitory efficacy against various types of cancer cells. Clerodendrum indicum and Clerodendrum serratum are reported to have anticancer activity; therefore a study was carried out to identify selective anticancer agents from these plants species. In this report, we employed a docking weighted network pharmacological approach to understand the multi-therapeutics potentiality of C. indicum and C. serratum against various types of cancer. A library of 53 natural products derived from these plants was compiled from the literature and three dimensional space analyses were performed in order to establish the drug-likeness of the NPs library. Further, an NPs-cancer network was built based on docking. We predicted five compounds, namely apigenin 7-glucoside, hispidulin, scutellarein-7-O-beta-d-glucuronate, acteoside and verbascoside, to be potential binding therapeutics for cancer target proteins. Apigenin 7-glucoside and hispidulin were found to have maximum binding interactions (relationship) with 17 cancer drug targets in terms of docking weighted network pharmacological analysis. Hence, we used an integrative approach obtained from network pharmacology for identifying combinatorial drug actions against the cancer targets. We believe that our present study may provide important clues for finding novel drug inhibitors for cancer.

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors.

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Veber's drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.