Research Title: Emerging therapeutic approaches to combat COVID-19: present status and future perspectives

Author: Balakumar Chandrasekarn, Published Year: 2021

Frontiers in Molecular Biosciences, 8

Faculty: Pharmacy

Abstract: Coronavirus disease (COVID-19) has emerged as a fast-paced epidemic in late 2019 which is disrupting life-saving immunization services. SARS-CoV-2 is a highly transmissible virus and an infectious disease that has caused fear among people across the world. The worldwide emergence and rapid expansion of SARS-CoV-2 emphasizes the need for exploring innovative therapeutic approaches to combat SARS-CoV-2. The efficacy of some antiviral drugs such as remdesivir, favipiravir, umifenovir, etc., are still tested against SARS-CoV-2. Additionally, there is a large global effort to develop vaccines for the protection against COVID-19. Because vaccines seem the best solution to control the pandemic but time is required for its development, pre-clinical/clinical trials, approval from FDA and scale-up. The nano-based approach is another promising approach to combat COVID-19 owing to unique physicochemical properties of nanomaterials. Peptide based vaccines emerged as promising vaccine candidates for SARS-CoV-2. The study emphasizes the current therapeutic approaches against SARS-CoV-2 and some of the potential candidates for SARS-CoV-2 treatment which are still under clinical studies for their effectiveness against SARS-CoV-2. Overall, it is of high importance to mention that clinical trials are necessary for confirming promising drug candidates and effective vaccines and the safety profile of the new components must be evaluated before translation of in vitro studies for implementation in clinical use.

Keywords: therapeutic approaches; COVID-19

Research Title: Computation in BioInformatics: Multidisciplinary Applications

Author: Balakumar Chandrasekarn, Published Year: 2021

Faculty: Pharmacy

Abstract: COMPUTATION IN BIOINFORMATICS Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design. The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development. Audience Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

Keywords: BioInformatics; Multidisciplinary Applications

Research Title: Handbook on nanobiomaterials for therapeutics and diagnostic applications

Author: Balakumar Chandrasekarn, Published Year: 2021

Faculty: Pharmacy

Abstract: Handbook on nanobiomaterials for therapeutics and diagnostic applications

Keywords: Handbook on nanobiomaterials for therapeutics and diagnostic applications

Research Title: Perspectives on RNA Vaccine Candidates for COVID-19

Author: Balakumar Chandrasekarn, Published Year: 2021

Frontiers in Molecular Biosciences, 8

Faculty: Pharmacy

Abstract: With the current outbreak caused by SARS-CoV-2, vaccination is acclaimed as a public health care priority. Rapid genetic sequencing of SARS-CoV-2 has triggered the scientific community to search for effective vaccines. Collaborative approaches from research institutes and biotech companies have acknowledged the use of viral proteins as potential vaccine candidates against COVID-19. Nucleic acid (DNA or RNA) vaccines are considered the next generation vaccines as they can be rapidly designed to encode any desirable viral sequence including the highly conserved antigen sequences. RNA vaccines being less prone to host genome integration (cons of DNA vaccines) and anti-vector immunity (a compromising factor of viral vectors) offer great potential as front-runners for universal COVID-19 vaccine. The proof of concept for RNA-based vaccines has already been proven in humans, and the prospects for commercialization are very encouraging as well. With the emergence of COVID-19, mRNA-1273, an mRNA vaccine developed by Moderna, Inc. was the first to enter human trials, with the first volunteer receiving the dose within 10 weeks after SARS-CoV-2 genetic sequencing. The recent interest in mRNA vaccines has been fueled by the state of the art technologies that enhance mRNA stability and improve vaccine delivery. Interestingly, as per the “Draft landscape of COVID-19 candidate vaccines” published by the World Health Organization (WHO) on December 29, 2020, seven potential RNA based COVID-19 vaccines are in different stages of clinical trials; of them, two candidates already received emergency use authorization, and another 22 potential candidates are undergoing pre-clinical investigations. This review will shed light on the rationality of RNA as a platform for vaccine development against COVID-19, highlighting the possible pros and cons, lessons learned from the past, and the future prospects.

Keywords: RNA Vaccine; COVID-19

Research Title: Developmental Landscape of Potential Vaccine Candidates Based on Viral Vector for Prophylaxis of COVID-19

Author: Balakumar Chandrasekarn, Published Year: 2021

Frontiers in Molecular Biosciences, 8

Faculty: Pharmacy

Abstract: 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.

Keywords: Vaccine Candidates; Viral Vector; COVID-19

Research Title: Prospective Role of Peptide-Based Antiviral Therapy Against the Main Protease of SARS-CoV-2

Author: Balakumar Chandrasekarn, Published Year: 2021

Frontiers in Molecular Biosciences, 8

Faculty: Pharmacy

Abstract: The recently emerged coronavirus (SARS-CoV-2) has created a crisis in world health, and economic sectors as an effective treatment or vaccine candidates are still developing. Besides, negative results in clinical trials and effective cheap solution against this deadly virus have brought new challenges. The viral protein, the main protease from SARS-CoV-2, can be effectively targeted due to its viral replication and pathogenesis role. In this study, we have enlisted 88 peptides from the AVPdb database. The peptide molecules were modeled to carry out the docking interactions. The four peptides molecules, P14, P39, P41, and P74, had more binding energy than the rest of the peptides in multiple docking programs. Interestingly, the active points of the main protease from SARS-CoV-2, Cys145, Leu141, Ser139, Phe140, Leu167, and Gln189, showed nonbonded interaction with the peptide molecules. The molecular dynamics simulation study was carried out for 200 ns to find out the docked complex’s stability where their stability index was proved to be positive compared to the apo and control complex. Our computational works based on peptide molecules may aid the future development of therapeutic options against SARS-CoV-2.

Keywords: Peptide-Based Antiviral Therapy; Protease; SARS-CoV-2

Research Title: In silico Screening of Natural Phytocompounds Towards Identification of Potential Lead Compounds to Treat COVID-19

Author: Balakumar Chandrasekarn, Published Year: 2021

Frontiers in Molecular Biosciences, 8

Faculty: Pharmacy

Abstract: COVID-19 is one of the members of the coronavirus family that can easily assail humans. As of now, 10 million people are infected and above two million people have died from COVID-19 globally. Over the past year, several researchers have made essential advances in discovering potential drugs. Up to now, no efficient drugs are available on the market. The present study aims to identify the potent phytocompounds from different medicinal plants (Zingiber officinale, Cuminum cyminum, Piper nigrum, Curcuma longa, and Allium sativum). In total, 227 phytocompounds were identified and screened against the proteins S-ACE2 and Mpro through structure-based virtual screening approaches. Based on the binding affinity score, 30 active phytocompounds were selected. Amongst, the binding affinity for beta-sitosterol and beta-elemene against S-ACE2 showed −12.0 and −10.9 kcal/mol, respectively. Meanwhile, the binding affinity for beta-sitosterol and beta-chlorogenin against Mpro was found to be −9.7 and −8.4 kcal/mol, respectively. Further, the selected compounds proceeded with molecular dynamics simulation, prime MM-GBSA analysis, and ADME/T property checks to understand the stability, interaction, conformational changes, binding free energy, and pharmaceutical relevant parameters. Moreover, the hotspot residues such as Lys31 and Lys353 for S-ACE2 and catalytic dyad His41 and Cys145 for Mpro were actively involved in the inhibition of viral entry. From the in silico analyses, we anticipate that this work could be valuable to ongoing novel drug discovery with potential treatment for COVID-19.

Keywords: n silico Screening; Natural Phytocompounds; Lead Compounds; COVID-19

Research Title: Recent targeted discovery of phytomedicines to manage endocrine disorder develops due to adapting sedentary lifestyle

Author: Balakumar Chandrasekarn, Published Year: 2023

Faculty: Pharmacy

Abstract: The endocrine system is a series of glands that produce and secrete hormones that the body uses for a wide range of functions. These control many different bodily functions

Keywords: Recent targeted discovery of phytomedicines

Research Title: Biosynthetic exosome nanoparticles isolation, characterization, and their diagnostic and therapeutic applications

Author: Balakumar Chandrasekarn, Published Year: 2021

Faculty: Pharmacy

Abstract: Exosomes are of emerging interest in diverse pathological conditions due to their key role in cellular physiology. These entities can signal and change the phenotype of target cells and hence they have quantitative and qualitative impacts on disease. Exosomes are organically found nanoparticles released endogenously by the cellular structure of mammals. Due to limitations like instability in donor cells, lower production, and incapability to target the required cells, exosomes are not utilized clinically

Keywords: Exosome; nanoparticles; isolation; characterization

Research Title: An insight of protein structure predictions using homology modelling

Author: Balakumar Chandrasekarn, Published Year: 2021

Faculty: Pharmacy

Abstract: If the query sequence has similar with template structure, then the model structure can be easily predicted with high resolution. The homology modeling for structural prediction plays a crucial role to discover the novel drug target against the various diseases. Based on the two important principles such as laws of physics and evolution, the 3D protein structure can be predicted. According to the physical and evaluation principles, protein folds have stable and well-formed structure via minimizing the energy and protein molecule has outcome of gradual changes in sequence and structure. The homology modeling has different multi steps which are most accurate to predict the absolute model structure. When the sequence identity is below 15%, it cannot be used for further structure modeling which could be lead to deceptive conclusion. The maximum similarity between 30% and 40% obtained from the query and template sequences can be considered for further homology modeling. If the similarity is above 50%, then the obtained model is adequate quality which can be used for further molecular docking for protein-protein docking, protein-ligand complexes, and molecular dynamic simulation studies. This modeling technique is very comfortable, faster, and cost-effective. This chapter will discuss the different homology model for the prediction of protein structure for the drug development process

Keywords: Homology modeling; methods and tools used for homology modeling