Current Topics in Medicinal Chemistry - Volume 22, Issue 26, 2022

Background: Rhus chinensis Mill, indigenous wild fruit primarily found in the hilly region of Nepal. The ripe fruit is very sour and considered medicinal as a remedy for colic pain. In addition, their astringent and styptic qualities are used internally to treat illnesses such as diarrhea and hemorrhage. Also, they are used as a common component of polyherbal medications for diabetic mellitus. Objectives: This work aimed to determine the total phenolic and flavonoid content, antioxidant, antibacterial, α-glucosidase, and α-amylase inhibition activity of the crude extract and fractions of Rhus chinensis Mill. Additionally, molecular docking of compounds from Rhus chinensis was performed. Methods: Folin Ciocalteu’s (FC) reagent was used to estimate total phenolic content. Likewise, the aluminium trichloride method was applied to determine total flavonoid content. A 2,2-diphenyl-1- picrylhydrazyl (DPPH) free radical scavenging assay was performed for the antioxidant activity. Furthermore, the substrate-based enzyme inhibition assay was carried out for α-glucosidase and α- amylase inhibition activity of R. chinensis. P-nitrophenyl-α-D-glucopyranoside (PNPG) and 2- Chloro-4-Nitrophenyl-α-D-Maltotrioside (CNPG3) were used as substrates for α-glucosidase and α- amylase inhibition assay, respectively. Similarly, the well-diffusion method was used for the antibacterial activity. Autodock vina was used to perform molecular docking. Results: The total phenolic and flavonoid content of R. chinensis fruit were 117.092±1.1 mg GAE/g and 62.41±1.23 mg QE/g, respectively. The IC50 value for antioxidant activity of the crude extract and its fractions ranged from 3.12±1.15μg/mL to 50.85±2.10μg/mL. Similarly, the IC50 for α- glucosidase inhibition ranged from2.33±1.01μg/mL to 28.34±2.79μg/mL. Likewise, The IC50 of R. chinensis crude methanolic extract against α-amylase was 120.3±1.382μg/mL. The antibacterial activity of R. chinensis was effective against gram-positive bacteria; Staphylococcus aureus (ZOI=11.0) and Bacillus subtilis (ZOI=9.0). Quercetin-3-O-rhamnoside and Myricetin-3-Orhamnoside showed excellent binding to the active site of protein with binding energy -9.4kcal/mol and -9.6kcal/mol, respectively. Conclusion: Rhus chinensis Mill is a potent antioxidant and inhibits enzymes; α-glucosidase and α- amylase. In addition, the methanolic extract of this plant shows antibacterial activity. However, further research is required to determine the inhibiting compounds.

Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disease characterized by progressive memory loss. The main pathological features of the disease are extracellular deposition of amyloid β (Aβ) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. Understanding factors contributing to AD progression, the number of molecular signatures, and the development of therapeutic agents played a significant role in the discovery of disease-modifying drugs to treat the disease. Bioinformatics has established its significance in many areas of biology. The role of bioinformatics in drug discovery, is emerging significantly and will continue to evolve. In recent years, different bioinformatics methodologies, viz. protein signaling pathway, molecular signature differences between different classes of drugs, interacting profiles of drugs and their potential therapeutic mechanisms, have been applied to identify potential therapeutic targets of AD. Bioinformatics tools were also found to contribute to the discovery of novel drugs, omics-based biomarkers, and drug repurposing for AD. The review aims to explore the applications of various advanced bioinformatics tools in the identification of targets, biomarkers, pathways, and potential therapeutics for the treatment of the disease.

The role of computational tools in the drug discovery and development process is becoming central, thanks to the possibility to analyze large amounts of data. The high throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets, has exponentially increased the volume of scientific data available. The quality of the data and the speed with which in silico predictions can be validated in vitro is instrumental in accelerating clinical laboratory medicine, significantly and substantially impacting Precision Medicine (PM). PM affords the basis to develop new drugs by providing a wide knowledge of the patient as an essential step towards individualized medicine. It is, therefore, essential to collect as much information and data as possible on each patient to identify the causes of the different responses to drugs from a pharmacogenomics perspective and to identify biological biomarkers capable of accurately describing the risk signals to develop specific diseases. Furthermore, the role of biomarkers in early drug discovery is increasing, as they can significantly reduce the time it takes to develop new drugs. This review article will discuss how Artificial Intelligence fits in the drug discovery pipeline, covering the benefits of an automated, integrated laboratory framework where the application of Machine Learning methodologies to interpret omics-based data can avail the future perspective of Translational Precision Medicine.

Over the last two decades, computational technologies have played a crucial role in antiviral drug development. Whenever a virus spreads and becomes a threat to global health, it brings along the challenge of developing new therapeutics and prophylactics. Computational drug and vaccine discovery has evolved quickly over the years. Some interesting examples of computational drug discovery are anti-AIDS drugs, where HIV protease and reverse transcriptase have been targeted by agents developed using computational methods. Various computational methods that have been applied to anti-viral research include ligand-based methods that rely on known active compounds, i.e., pharmacophore modeling, machine learning or classical QSAR; structure-based methods that rely on an experimentally determined 3D structure of the targets, i.e., molecular docking and molecular dynamics and methods for the development of vaccines such as reverse vaccinology; structural vaccinology and vaccine epitope prediction. This review summarizes these approaches to battle viral diseases and underscores their importance for anti-viral research. We discuss the role of computational methods in developing small molecules and vaccines against human immunodeficiency virus, yellow fever, human papilloma virus, SARS-CoV-2, and other viruses. Various computational tools available for the abovementioned purposes have been listed and described. A discussion on applying artificial intelligence-based methods for antiviral drug discovery has also been included.

Clinical translation is a challenging step in the development of cancer vaccines and is found to be related to the complex nature of cancer immunology. Vaccine-based therapeutic strategies for cancer have gained consideration with the advent of vaccine technology as well as an understanding of cancer immunology. Immunotherapy has been widely used in the treatment of cancer. Some promising candidates have been identified to engineer cancer vaccines like Glycoprotein, Mucin 1, MHC protein, etc. It has benefited from the availability of advanced techniques for rapid identification and selection of proteins for precision engineering. Simultaneously, nanovaccines have been focused on target delivery and artificial intelligence-based approaches for personalized vaccine development. The manuscript summarizes the advances in the development of structurebased cancer vaccines along with the status of clinical studies and applications.