SARS-CoV-2 host cell entry: an in silico investigation of potential inhibitory roles of terpenoids

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Integrated Computational Approaches for Inhibiting Sex Hormone-Binding Globulin in Male Infertility by Screening Potent Phytochemicals

Male infertility is significantly influenced by the plasma-protein sex hormone-binding globulin (SHBG). Male infertility, erectile dysfunction, prostate cancer, and several other male reproductive system diseases are all caused by reduced testosterone bioavailability due to its binding to SHBG. In this study, we have identified 345 phytochemicals from 200 literature reviews that potentially inhibit severe acute respiratory syndrome coronavirus 2. Only a few studies have been done using the SARS-CoV-2 inhibitors to identify the SHBG inhibitor, which is thought to be the main protein responsible for male infertility. In virtual-screening and molecular-docking experiments, cryptomisrine, dorsilurin E, and isoiguesterin were identified as potential SHBG inhibitors with binding affinities of -9.2, -9.0, and -8.8 kcal/mol, respectively. They were also found to have higher binding affinities than the control drug anastrozole (-7.0 kcal/mol). In addition to favorable pharmacological properties, these top three phytochemicals showed no adverse effects in pharmacokinetic evaluations. Several molecular dynamics simulation profiles' root-mean-square deviation, radius of gyration, root-mean-square fluctuation, hydrogen bonds, and solvent-accessible surface area supported the top three protein-ligand complexes' better firmness and stability than the control drug throughout the 100 ns simulation period. These combinatorial drug-design approaches indicate that these three phytochemicals could be developed as potential drugs to treat male infertility.

Modern drug discovery applications for the identification of novel candidates for COVID-19 infections

In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.

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Importance of advanced genome sequencing in analyzing the sequential change in DNA and RNA due to SARS-CoV-2 genome.

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Lead Identification studies with computer drug-aided design.

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Prevalence of some flavonoid glycosides in medicinal plants which have potential protective effects against COVID-19 infections.

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Docking studies with alkaloids, vanillin derivatives and their actions suggest possible SARS- CoV-2 inhibition.

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Modeling studies outcomes about ligand-based and structure based activity.

Nanoscale Technologies in the Fight against COVID-19: From Innovative Nanomaterials to Computer-Aided Discovery of Potential Antiviral Plant-Derived Drugs

The last few years have increasingly emphasized the need to develop new active antiviral products obtained from artificial synthesis processes using nanomaterials, but also derived from natural matrices. At the same time, advanced computational approaches have found themselves fundamental in the repurposing of active therapeutics or for reducing the very long developing phases of new drugs discovery, which represents a real limitation, especially in the case of pandemics. The first part of the review is focused on the most innovative nanomaterials promising both in the field of therapeutic agents, as well as measures to control virus spread (i.e., innovative antiviral textiles). The second part of the review aims to show how computer-aided technologies can allow us to identify, in a rapid and therefore constantly updated way, plant-derived molecules (i.e., those included in terpenoids) potentially able to efficiently interact with SARS-CoV-2 cell penetration pathways.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

A review on garcinia kola heckel: traditional uses, phytochemistry, pharmacological activities, and toxicology

PURPOSE

Garcinia kola is a medicinal plant commonly known as bitter kola. It is utilised in ethnomedicine for the treatment of diarrhoea, bronchitis, bacterial infection, cough, hepatitis, gonorrhoea, laryngitis, food poison, liver and gastric diseases.

OBJECTIVE

This study reviewed the phytochemistry, pharmacological activities, and ethnomedicinal potentials of G. kola.

MATERIALS AND METHODS

An extensive review was performed using electronic literature collated from ScienceDirect, Springer, Wiley, and PubMed databases.

RESULTS

Phytochemical analysis revealed the isolation of several chemical compounds including 9-octadecenoic acid, linoleic acid, 14-methylpentadecanoic acid, 1-butanol, hexadecanamide, I-4',II-4',I-5,II-5,I-7,II-7-hexahydroxy-I-3,II-8-biflavanone, lanost-7-en-3-one, kolaflavanone (8E)-4-geranyl-3,5-dihydroxybenzophenone, glutinol, Garcinia biflavonoid (GB-2a-II-4'-OMe), 9,19-cyclolanost-24-en-3-ol, 24-methylene, tirucallol, lupeol, β-amyrin, obtusifoliol and Kolaviron. Diverse pharmacological in-vivo and in vitro investigations revealed that G. kola has anti-inflammatory, antimalarial, hepatoprotective, cardioprotective, anti-asthmatic, neuroprotective, antioxidant, and antidiabetic activities.

CONCLUSION

The present study revealed that G. kola has preventive and therapeutic potentials against various diseases in both in vivo and in vitro studies and therefore can be utilised as a raw material in the pharmaceutical industries for the development of therapeutic products. However, there is a need for clinical trial experiments to validate and provide accurate and substantial information on the required safe dosage and efficacy for the treatment of several diseases.