BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Has files: NoSubject: search.filters.subject.COVID-19

Search Results

Now showing 1 - 10 of 83
  • No Thumbnail Available
    Item
    Impacts of COVID-19 pandemic on the wastewater pathway into surface water: a review
    (Elsevier, 2021-06) Goonetilleke, Ashantha
    With global number of cases 106 million and death toll surpassing 2.3 million as of mid-February 2021, the COVID-19 pandemic is certainly one of the major threats that humankind have faced in modern history. As the scientific community navigates through the overwhelming avalanche of information on the multiple health impacts caused by the pandemic, new reports start to emerge on significant ancillary effects associated with the treatment of the virus. Besides the evident health impacts, other emerging impacts related to the COVID-19 pandemic, such as water-related impacts, merits in-depth investigation. This includes strategies for the identification of these impacts and technologies to mitigate them, and to prevent further impacts not only in water ecosystems, but also in relation to human health. This paper has critically reviewed currently available knowledge on the most significant potential impacts of the COVID-19 pandemic on the wastewater pathway into surface water, as well as technologies that may serve to counteract the major threats posed, key perspectives and challenges. Additionally, current knowledge gaps and potential directions for further research and development are identified. While the COVID-19 pandemic is an ongoing and rapidly evolving situation, compiling current knowledge of potential links between wastewater and surface water pathways as related to environmental impacts and relevant associated technologies, as presented in this review, is a critical step to guide future research in this area.
  • No Thumbnail Available
    Item
    New normal of stock trading in India
    (Bloomsbury Publishing, 2021-02) Tiwary, Daitri
    The stock markets around the world witnessed bloodbath in the month of March 2020 as painful news about the spread of the novel coronavirus sustained to accumulate. The immense disruption caused by COVID-19 has projected the post pandemic stock market scenario into uncharted territory. In the current pandemic scenario, the current stock market has seen its highs and lows affected by the crisis and the liquidity in the market, with the performance and operations of companies taking greater hit. In the post pandemic era, stock markets are expected to pose lot of opportunities driven by deep research analysis and market insights. What is pertinent is disruption in trading strategies in the post COVID-19 world. Looking out for future opportunities and challenges, the volatility of the stocks in the post-pandemic market is explore and the deviations from traditional trading strategies are analyzed; the aim is to prescribe the investors to plot their course through the “new normal” of capital market. The articles will be exploring investor sentiments coupled with SENSEX data to probe deeper for insights. The market has also witnessed healthcare and pharma stocks being caught in a frenzy. The article attempts to analyze the development of vaccine as well as supportive healthcare as a treatment of the novel coronavirus with an aim to predict the returns from pharma stocks. Converging the present trend of stock analysis, the course of rebound is explored, given the success of a vaccine. The authors explore how the new normal demands a ‘New Mindset’for traders and investors. While it is still believed that the current COVID-19 challenges are temporary
  • No Thumbnail Available
    Item
    Use of stem cell-derived cardiomyocyte and nasal epithelium models to establish a multi-tissue model platform to validate repurposed drugs against sars-cov-2 infection
    (2024-05) Agarwal, Vinti
    The novel coronavirus disease (COVID-19) and any future coronavirus outbreaks will require more affordable, effective and safe treatment options to complement current ones such as Paxlovid. Drug repurposing can be a promising approach if we are able to find a rapid, robust and reliable way to down-select and screen candidates using in silico and in vitro approaches. With repurposed drugs, ex vivo models could offer a rigorous route to human clinical trials with less time invested into nonclinical animal (in vivo) studies. We have previously shown the value of commercially available ex vivo/3D airway and alveolar tissue models, and this paper takes this further by developing and validating human nasal epithelial model and embryonic stem cells derived cardiomyocyte model. Five shortlisted candidates (fluvoxamine, everolimus, pyrimethamine, aprepitant and sirolimus) were successfully compared with three control drugs (remdesivir, molnupiravir, nirmatrelvir) when tested against key variants of the SARS-CoV-2 virus including Delta and Omicron, and we were able to reconfirm our earlier finding that fluvoxamine can induce antiviral efficacy in combination with other drugs. Scalability of this high-throughput screening approach has been demonstrated using a liquid handling robotic platform for future ‘Disease-X’ outbreaks.
  • No Thumbnail Available
    Item
    In silico evaluation of bisphosphonates identifies leading candidates for SARS-CoV-2 RdRp inhibition
    (Elsevier, 2025-05) Garg, Mohit; Murugesan, Sankaranarayanan
    The novel coronavirus disease (COVID-19) pandemic has resulted in 777 million confirmed cases and over 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Post Acute Sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. In this study, we have investigated all bisphosphonates (BPs) on the ChEMBL database which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies in Japan with minodronate could be valuable.
  • No Thumbnail Available
    Item
    Modeling the effect of vaccinations, hospital beds, and treatments on the dynamics of infectious disease with delayed optimal control and sensitivity analysis
    (Springer, 2024-08) Dubey, Uma S.; Dubey, Balram
    Immunization plays a vital role in eradicating infectious diseases, typically requiring multiple doses at specific time intervals. This study focuses on developing and analyzing an infectious disease model governed by a six-dimensional system of ordinary differential equations, considering the impact of first and second vaccination doses along with hospital beds and treatment. The model’s qualitative behavior is analyzed, including conditions for positive solutions, the invariant region of the solution, equilibrium points, and their stability. When the basic reproduction number () is less than one (), the disease will be eradicated; conversely, an epidemic occurs when . Moreover, the transcritical bifurcation of the system is examined using the center manifold theory. Interestingly, backward bifurcation is discovered, and it indicates that the disease is not entirely eradicated even when . We have investigated different bifurcations like saddle-node, transcritical, and Hopf bifurcations of codimension 1, as well as Generalized-Hopf (GH), Cusp (CP), and Bogdanov–Takens (BT) bifurcations of codimension 2. Additionally, a delayed epidemiological model is explored, assuming a lag in vaccination among the susceptible population. A Hopf-bifurcation is observed near the endemic equilibrium point, linked to critical parameter values during the latent period. Moreover, the model is calibrated using the least-squares technique, incorporating coronavirus-infected case data and vaccination information from India and Italy’s mass vaccination program between March 1, 2021, and May 30, 2021. Global sensitivity analysis, utilizing the Partial Rank Correlation Coefficient (PRCC), identifies crucial parameters affecting threshold quantities after fitting the model. The study highlights the significance of critical parameters such as the effective transmission rate, rates of first and second-dose vaccinations, and recovery rate due to double-dose vaccination. Further, delayed optimal control measures are determined using Pontryagin’s maximal principle to mitigate infection, prevention, and treatment burdens. Numerical simulations are conducted to understand the effect of these delayed control measures on disease progression and demonstrate the insights obtained through analytical investigations. The study indicates that implementing all control strategies effectively reduces the disease burden among the population. Accurate estimation of vaccine efficacy is crucial for disease prevention, underlining the importance of well-planned vaccination strategies. Moreover, the numerical simulations validate all the theoretical findings, emphasizing the validity of this model in a real-world situation. Relying solely on vaccination might not swiftly or completely control the disease. Complementary pharmaceutical and non-pharmaceutical measures are necessary to combat the infection effectively. Further limitations on medical resources could lead to a backward bifurcation. Simulation results suggest that delaying the implementation of control measures could exacerbate epidemic situations.
  • No Thumbnail Available
    Item
    Introduction to health informatics (hi): enhancing healthcare through information technology
    (Springer, 2024) Sundriyal, Sandeep
    Health informatics (HI) is an interdisciplinary field that integrates data science, information technology, and healthcare to enhance the administration and delivery of healthcare services. It includes the storage, visualization, and analytics of electronic health records, thus assisting in improving patient care, enhancing clinical decision-making, facilitating data exchange, and supporting research and public health initiatives. This chapter provides an overview of the fundamental concepts of HI and its role in reforming the healthcare system. The basic challenges and ethical issues in implementing HI are also examined in this chapter. In addition, glimpses of HI's research and development activities are provided, employing the case study of the COVID-19 pandemic. This chapter would be helpful for beginners as well as healthcare professionals to get acquainted with the principles and applications of HI.
  • No Thumbnail Available
    Item
    Computational search for potential covid-19 drugs from ayurvedic medicinal plants to identify potential inhibitors against sars-cov-2 targets
    (Bentham Science, 2023-02) Murugesan, Sankaranarayanan
    To date, very few small drug molecules are used for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has been discovered since the epidemic commenced in November 2019. SARS-CoV-2 RdRp and spike protein are essential targets for drug development amidst whole variants of coronaviruses. Objective: This study aims to discover and recognize the most effective and promising small molecules against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and spike protein targets through molecular docking screening of 39 phytochemicals from five different Ayurveda medicinal plants. Methods: The phytochemicals were downloaded from PubChem, and SARS-CoV-2 RdRp and spike protein were taken from the protein data bank. The molecular interactions, binding energy, and ADMET properties were analyzed. Results: Molecular docking analysis identified some phytochemicals, oleanolic acid, friedelin, serratagenic acid, uncinatone, clemaphenol A, sennosides B, trilobine and isotrilobine from ayurvedic medicinal plants possessing greater affinity against SARS-CoV-2-RdRp and spike protein targets. Two molecules, namely oleanolic acid and sennosides B, with low binding energies, were the most promising. Furthermore, based on the docking score, we carried out MD simulations for the oleanolic acid and sennosides B-protein complexes. Conclusion: Molecular ADMET profile estimation showed that the docked phytochemicals were safe. The present study suggested that active phytochemicals from medicinal plants could inhibit RdRp and spike protein of SARS-CoV-2.
  • No Thumbnail Available
    Item
    Drug repurposing: an effective tool in modern drug discovery
    (Springer, 2023-02) Murugesan, Sankaranarayanan
    Drug repurposing is using an existing drug for a new treatment that was not indicated before. It has received immense attention during the COVID-19 pandemic emergency. Drug repurposing has become the need of time to fasten the drug discovery process and find quicker solutions to the over-exerted healthcare scenario and drug needs. Drug repurposing involves identifying the drug, evaluating its efficiency using preclinical models, and proceeding to phase II clinical trials. Identification of the drug candidate can be made through computational and experimental approaches. This approach usually utilizes public databases for drugs. Data from primary and translational research, clinical trials, anecdotal reports regarding off-label uses, and other published human data information available are included. Using artificial intelligence algorithms and other bioinformatics tools, investigators systematically try to identify the interaction between drugs and protein targets. It can be combined with genetic data, clinical analysis, structure (molecular docking), pathways, signatures, targets, phenotypes, binding assays, and artificial intelligence to get an optimum outcome in repurposing. This article describes the strategies involved in drug repurposing and enlists a series of repurposed drugs and their indications.
  • No Thumbnail Available
    Item
    An in silico investigation to identify promising inhibitors for sars-cov-2 mpro target
    (Bentham Science, 2023-11) Murugesan, Sankaranarayanan
    A limited number of small molecules against SARS-CoV-2 has been discovered since the epidemic commenced in November 2019. The conventional medicinal chemistry approach demands more than a decade of the year of laborious research and development and a substantial financial commitment, which is not achievable in the face of the current epidemic. Objective: This study aims to discover and recognize the most effective and promising small molecules by interacting SARS-CoV-2 Mpro target through computational screening of 39 phytochemicals from five different Ayurvedic medicinal plants. Methods: The phytochemicals were downloaded from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) PubChem, and the SARS-CoV-2 protein (PDB ID: 6LU7; Mpro) was taken from the PDB. The molecular interactions, binding energy, and ADMET properties were analyzed. Results: The binding affinities were studied using a structure-based drug design of molecular docking, divulging 21 molecules possessing greater to equal affinity towards the target than the reference standard. Molecular docking analysis identified 13 phytochemicals, sennoside-B (-9.5 kcal/mol), isotrilobine (-9.4 kcal/mol), trilobine (-9.0 kcal/mol), serratagenic acid (-8.1 kcal/mol), fistulin (-8.0 kcal/mol), friedelin (-7.9 kcal/mol), oleanolic acid (-7.9 kcal/mol), uncinatone (-7.8 kcal/mol), 3,4-di- O-caffeoylquinic acid (-7.4 kcal/mol), clemaphenol A (-7.3 kcal/mol), pectolinarigenin (-7.2 kcal/mol), leucocyanidin (-7.2 kcal/mol), and 28-acetyl botulin (-7.2 kcal/mol) from ayurvedic medicinal plants phytochemicals possess greater affinity than the reference standard Molnupiravir (-7.0 kcal/mol) against SARS-CoV-2-Mpro. Conclusion: Two molecules, namely sennoside-B, and isotrilobine with low binding energies, were predicted as most promising. Furthermore, we carried out molecular dynamics simulations for the sennoside-B protein complexes based on the docking score. ADMET properties prediction confirmed that the selected docked phytochemicals were optimal. These compounds can be investigated further and utilized as a parent core molecule to create novel lead molecules for preventing COVID-19.
  • No Thumbnail Available
    Item
    Potential inhibitors from natural compounds against sars-cov-2 main protease: a systematic molecular modelling approach
    (Wiley, 2024-02) Murugesan, Sankaranarayanan
    The COVID-19 outbreak poses a significant threat to the world‘s human population in 2020. Finding new drugs rapidly during this pandemic is quite challenging. Thus, in silico drug screening experiments may provide effective therapeutic alternatives for better assessing natural remedies in preventing and treating COVID-19. The main protease (Mpro) is an important drug target that is essential and ubiquitous for the survival of SARS-CoV-2. In this study, we performed in silico high-throughput virtual screening to identify potential hits employing a database of 3 million natural compounds (supernatural-II database). The initially obtained top 100 virtual hits were subjected to a standard SP and XP docking protocol, achieving the top 30 hits. Compounds SN00340755 (glide score: −16.0 kcal/mol and ΔGbind: −134.29 kcal/mol) and SN00213037 (glide score: −13.30 kcal/mol and ΔGbind: −81.18 kcal/mol) exhibited significant binding energy against Mpro (PDB ID: 6XQS). The ligands SN00340755 and SN00213037 formed multiple hydrogen bonds with the catalytic residues, especially with the functionally important residue GLU166, which plays a significant role in protomer dimerization. Further post-docking minimization studies (MM-GBSA) were performed to estimate the ligand-protein affinity. From MM-GBSA studies, it was observed that Coulombic (−140.70 to −37.66 kcal/mol) and van der Waals (−79.32 to −20.59 kcal/mol) energies, favoring the binding of ligands to the Mpro target protein. The ADMET properties were predicted using Qikprop, Chem Axon, and Data Warrior tools, demonstrating the beneficial pharmacokinetic parameters of these natural compounds. The 100 ns molecular dynamics simulation study revealed minor protein fluctuations, indicating the stability of the protein-ligand complex.