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Background: High prevalence, severity, and formidable morbidity have marked the recent emergence of the novel coronavirus disease (COVID-19) pandemic. The significant association with the pre-existing co-morbid conditions has increased the disease burden of this global health emergency, pushing the patients, healthcare workers and facilities to the verge of complete disruption. Methods: Meta-analysis of pooled data was undertaken to assess the cumulative risk assessment of multiple co-morbid conditions associated with severe COVID-19. PubMed, Scopus, and Google Scholar were searched from January 1st to June 27th 2020 to generate a well-ordered, analytical, and critical review. The exercise began with keying in requisite keywords, followed by inclusion and exclusion criteria, data extraction, and quality evaluation. The final statistical meta-analysis of the risk factors of critical/severe and non-critical COVID-19 infection was carried out on Microsoft Excel (Ver. 2013), MedCalc (Ver.19.3), and RevMan software (Ver.5.3). Results: We investigated 19 eligible studies, comprising 12037 COVID-19 disease patients, representing the People’s Republic of China (PRC), USA, and Europe. 18.2% (n = 2200) of total patients had critical/severe COVID-19 disease. The pooled analysis showed a significant association of COVID-19 disease severity risk with cardiovascular disease (RR: 3.11, p < 0.001), followed by diabetes (RR: 2.06, p < 0.001), hypertension (RR: 1.54, p < 0.001), and smoking (RR: 1.52, p < 006). Conclusion: The review involved a sample size of 12037 COVID-19 patients across a wide geographical distribution. The reviewed reports have focused on the association of individual risk assessment of co-morbid conditions with the heightened risk of COVID-19 disease. The present meta-analysis of cumulative risk assessment of co-morbidity from cardiovascular disease, diabetes, hypertension, and smoking signals a novel interpretation of inherent risk factors exacerbating COVID-19 disease severity. Consequently, there exists a definite window of opportunity for increasing survival of COVID-19 patients (with high risk and co-morbid conditions) by timely identification and implementation of appropriately suitable treatment modalities.

Increased numbers of reported cases of Mycobacterium tuberculosis (Mtb) resistance to the generally used antibiotics demand to identify novel therapeutic entities for better control of Tuberculosis. Most of the Structure-based Drug Discovery (SBDD) works reported earlier had screened compounds against a single drug target to avoid any off-target binding and related complications. Because of the development of Multi-Drug Resistant and Extensively Drug-Resistant strains of Mtb and looking into the incurable pathologies, targeting the right drug target with a promising ligand data set will result in effective therapeutics. Simultaneously, traditional knowledge-based drugs have earned little success in developing anti-tuberculosis drugs in recent studies. Combining the right-target approach and traditional herbal knowledge base, this in silico drug discovery study was carried out where 1236 compounds from two plants, traditionally used for TB treatment, Camellia sinensis, Ginkgo biloba along with the antibacterial compounds of DrugBank Database have been screened against Pantothenate synthetase of Mtb, a well-known drug target for anti-TB therapeutics. Through this analytics, Epigallocatechin gallate (EGCG) of Camellia sinensis has been reported through in silico docking studies and subsequent Molecular Dynamics simulation, as a promising anti-TB candidate due to its affinity towards Pantothenate synthetase of Mtb. EGCG was subjected to ADME-Tox studies as well as 3D QSAR analysis for the detection of its drug-like properties and for the determination of IC50 value. The EGCG showed the IC50 value at 1404 nM, which is quite promising for a plant-origin compound. The selected ligand, EGCG, due to its promising affinity towards Pantothenate synthetase of Mtb with high drug-like properties, justifies its selection as a potential anti-tuberculosis compound.

The incurable Type 2 diabetes mellitus (T2DM) has now been considered a pandemic with only supportive care in existence. Due to the adverse effects of available anti-diabetic drugs, there arises a great urgency to develop new drug molecules. One of the alternatives that can be considered for the treatment of T2DM are natural compounds from traditionally used herbal medicine. The present study undertakes, an integrated multidisciplinary concept of Network Pharmacology to evaluate the efficacy of potent anti-diabetic compound from traditionally used anti-diabetic plants of north east India and followed by DFT analysis. In the course of the study, 22 plant species were selected on the basis of their use in traditional medicine for the treatment of T2DM by various ethnic groups of the north eastern region of India. Initially, a library of 1053 compounds derived from these plants was generated. This was followed by network preparation between compounds and targets based on the docking result. The compounds having the best network property were considered for DFT analysis. We have identified that auraptene, a monoterpene coumarin for its activity in the management of Type 2 diabetes mellitus and deciphered its unexplored probable mechanisms. Molecular dynamics simulation of the ligand–protein complexes also reveals the stable binding of auraptene with the target proteins namely, Protein Kinase C θ, Glucocorticoid receptor, 11-β hydroxysteroid dehydrogenase 1 and Aldose Reductase, all of which form uniform interactions throughout the MD simulation trajectory. Therefore, this finding could provide new insights for the development of a new anti-diabetic drug. Communicated by Ramaswamy H. Sarma