Saponins in Cancer Treatment: Current Progress and Future Prospects

Saponins are steroidal or triterpenoid glycoside that is distinguished by the soap-forming nature. Different saponins have been characterized and purified and are gaining attention in cancer chemotherapy. Saponins possess high structural diversity, which is linked to the anticancer activities. Several studies have reported the role of saponins in cancer and the mechanism of actions, including cell-cycle arrest, antioxidant activity, cellular invasion inhibition, induction of apoptosis and autophagy. Despite the extensive research and significant anticancer effects of saponins, there are currently no known FDA-approved saponin-based anticancer drugs. This can be attributed to a number of limitations, including toxicities and drug-likeness properties. Recent studies have explored options such as combination therapy and drug delivery systems to ensure increased efficacy and decreased toxicity in saponin. This review discusses the current knowledge on different saponins, their anticancer activity and mechanisms of action, as well as promising research within the last two decades and recommendations for future studies

Computational study of the therapeutic potentials of a new series of imidazole derivatives against SARS-CoV-2

Owing to the urgent need for therapeutic interventions against the SARS-coronavirus 2 (SARS-CoV-2) pandemic, we employed an in silico approach to evaluate the SARS-CoV-2 inhibitory potential of newly synthesized imidazoles. The inhibitory potentials of the compounds against SARS-CoV-2 drug targets - main protease (Mpro), spike protein (Spro) and RNA-dependent RNA polymerase (RdRp) were investigated through molecular docking analysis. The binding free energy of the protein-ligand complexes were estimated, pharmacophore models were generated and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of the compounds were determined. The compounds displayed various levels of binding affinities for the SARS-CoV-2 drug targets. Bisimidazole C2 scored highest against all the targets, with its aromatic rings including the two imidazole groups contributing to the binding. Among the phenyl-substituted 1H-imidazoles, C9 scored highest against all targets. C11 scored highest against Spro and C12 against Mpro and RdRp among the thiophene-imidazoles. The compounds interacted with HIS 41 - CYS 145 and GLU 288 – ASP 289 – GLU 290 of Mpro, ASN 501 of Spro receptor binding motif and some active site amino acids of RdRp. These novel imidazole compounds could be further developed as drug candidates against SARS-CoV-2 following lead optimization and experimental studies

Discovery of potential visfatin activators using in silico docking and ADME predictions as therapy for type 2 diabetes

Visfatin (Nicotinamide phosphoribosyltransferase) is an adipokine implicated in mediating insulin resistance and exhibiting insulin mimetic effect and therefore represents a druggable target for diabetes therapy. About 3,844 peroxisome proliferator activated receptor gamma (PPARγ) agonists documented in Chembl database were docked with PPARγ and those with binding energy of >−9 kcal/mol having experimental EC50 of 0.1 to 1 nM were selected. The candidate compounds (27) were thereafter docked with visfatin (PDB ID: 4WQ6) using AutodockVina out of which eight compounds that ranked highest in binding energy (when compared with the co-crystallized ligand of visfatin: 3TQ) were selected. Compound 25 exhibited favorable ligand-protein molecular interaction and respected Lipinski’s rule of five and interestingly from the absorption, distribution, metabolism and excretion (ADME)-Toxicity analysis the compound have enhanced pharmacological properties than the current ligand of visfatin. Keywords: Nicotinamide phosphoribosyltransferase, Visfatin molecular docking, Type 2 diabetes, Adipokine

Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward?

The escalating prevalence of cancer on a global scale, coupled with the inadequacies of present-day therapies and the emergence of drug-resistant cancer strains, has necessitated the development of additional anticancer drugs. The traditional drug discovery process is long and complex, and the high failure rate of new drugs in clinical trials further highlights the need for computational approaches in anticancer drug discovery. Computer-aided drug design (CADD), including molecular docking, molecular dynamics simulations, QSAR analysis, and machine learning, are employed to predict the efficacy of potential drug compounds and pinpoint the most auspicious compounds for subsequent testing and advancement. This article provides an overview of contemporary computational approaches employed in the design of anti-cancer drugs. It highlights a range of small molecules that have been identified as capable of impeding cancer growth and migration through various mechanisms, including cell cycle arrest/apoptosis, signal transduction inhibition, angiogenesis, epigenetics, and the hedgehog pathway. It also examines the constraints of computational techniques and presents remedies to surmount these limitations in the development and identification of efficacious anticancer compounds