Search Results (6)

Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, frequently arising from platelet hyperactivation and subsequent thrombus formation. Although conventional antiplatelet therapies are available, challenges, such as drug resistance and bleeding complications, require the development of novel agents. In this study, dihydrogeodin (DHG) was isolated from Fennellia flavipes and evaluated using platelets derived from Sprague–Dawley rats. Platelet aggregation induced by collagen, adenosine diphosphate, or thrombin was assessed by light transmission aggregometry; DHG significantly reduced aggregation in a dose-dependent manner. Further assays demonstrated that DHG suppressed intracellular calcium mobilization, adenosine triphosphate release, and integrin αIIbβ₃-dependent fibrinogen binding, thereby impairing clot retraction. Western blot analysis revealed that DHG reduced the phosphorylation of mitogen-activated protein kinases (ERK, JNK, p38) and PI3K/Akt, indicating inhibition across multiple platelet-signaling pathways. Additionally, SwissADME-assisted pharmacokinetics predicted favorable properties without violations of the Lipinski (Pfizer) filter, Muegge (Bayer) filter, Ghose filter, Veber filter, and Egan filter, and network pharmacology revealed inhibition of calcium and MAPK pathways. These results highlight the potential of DHG as a novel antiplatelet agent with broad-spectrum activity and promising drug-like characteristics. Further studies are warranted to assess its therapeutic window, safety profile, and potential for synergistic use with existing antiplatelet drugs. Full article

Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. Full article

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps. Full article

Among the various types of cancer, lung cancer is the second most-diagnosed cancer worldwide. The kinesin spindle protein, Eg5, is a vital protein behind bipolar mitotic spindle establishment and maintenance during mitosis. Eg5 has been reported to contribute to cancer cell migration and angiogenesis impairment and has no role in resting, non-dividing cells. Thus, it could be considered as a vital target against several cancers, such as renal cancer, lung cancer, urothelial carcinoma, prostate cancer, squamous cell carcinoma, etc. In recent years, fungal secondary metabolites from the Indian Himalayan Region (IHR) have been identified as an important lead source in the drug development pipeline. Therefore, the present study aims to identify potential mycotic secondary metabolites against the Eg5 protein by applying integrated machine learning, chemoinformatics based in silico-screening methods and molecular dynamic simulation targeting lung cancer. Initially, a library of 1830 mycotic secondary metabolites was screened by a predictive machine-learning model developed based on the random forest algorithm with high sensitivity (1) and an ROC area of 0.99. Further, 319 out of 1830 compounds screened with active potential by the model were evaluated for their drug-likeness properties by applying four filters simultaneously, viz., Lipinski’s rule, CMC-50 like rule, Veber rule, and Ghose filter. A total of 13 compounds passed from all the above filters were considered for molecular docking, functional group analysis, and cell line cytotoxicity prediction. Finally, four hit mycotic secondary metabolites found in fungi from the IHR were screened viz., (−)-Cochlactone-A, Phelligridin C, Sterenin E, and Cyathusal A. All compounds have efficient binding potential with Eg5, containing functional groups like aromatic rings, rings, carboxylic acid esters, and carbonyl and with cell line cytotoxicity against lung cancer cell lines, namely, MCF-7, NCI-H226, NCI-H522, A549, and NCI H187. Further, the molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, and radius of gyration analysis from 100 ns simulation trajectories. The screened compounds could be used further to develop effective drugs against lung and other types of cancer. Full article

Homeostatic trafficking of immune cells by CC chemokine receptor 7 (CCR7) keeps immune responses and tolerance in a balance. The involvement of this protein in lymph node metastasis in cancer marks CCR7 as a penitential drug target. Using the crystal structure of CCR7, herein, a comprehensive virtual screening study is presented to filter novel strong CCR7 binding phytochemicals from Saudi medicinal plants that have a higher binding affinity for the intracellular allosteric binding pocket. By doing so, three small natural molecules named as Hit-1 (1,8,10-trihydroxy-3-methoxy-6-methylanthracen-9(4H)-one), Hit-2 (4-(3,4-dimethoxybenzyl)-3-(4-hydroxy-3-methoxybenzyl)dihydrofuran-2(3H)-one), and Hit-3 (10-methyl-12,13-dihydro-[1,2]dioxolo[3,4,5-de]furo[3,2-g]isochromeno[4,3-b]chromen-8-ol) are predicted showing strong binding potential for the CC chemokine receptor 7 allosteric pocket. During molecular dynamics simulations, the compounds were observed in the formation of several chemical bonding of short bond distances. Additionally, the molecules remained in strong contact with the active pocket residues and experienced small conformation changes that seemed to be mediated by the CCR7 loops to properly engage the ligands. Two types of binding energy methods (MM/GBPBSA and WaterSwap) were additionally applied to further validate docking and simulation findings. Both analyses complement the good affinity of compounds for CCR7, the electrostatic and van der Waals energies being the most dominant in intermolecular interactions. The active pocket residue’s role in compounds binding was further evaluated via alanine scanning, which highlighted their importance in natural compounds binding. Additionally, the compounds fulfilled all drug-like rules: Lipinski, Ghose, Veber, Egan, and Muegge passed many safety parameters, making them excellent anti-cancer candidates for experimental testing. Full article

Phytonutrients are plant foods that contain many natural bioactive compounds, called phytochemicals, which show specific biological activities. These phytonutrients and their phytochemicals may play an important role in health care maintaining normal organism functions (as preventives) and fighting against diseases (as therapeutics). Phytonutrients’ components are the primary metabolites (i.e., proteins, carbohydrates, and lipids) and phytochemicals or secondary metabolites (i.e., phenolics, alkaloids, organosulfides, and terpenes). For years, several phytonutrients and their phytochemicals have demonstrated specific pharmacological and therapeutic effects in human health such as anticancer, antioxidant, antiviral, anti-inflammatory, antibacterial, antifungal, and immune response. This review summarizes the effects of the most studied or the most popular phytonutrients (i.e., turmeric, garlic, cinnamon, graviola, and oregano) and any reported contraindications. This article also presents the calculated physicochemical properties of the main phytochemicals in the selected phytonutrients using Lipinski’s, Veber’s, and Ghose’s rules. Based on our revisions for this article, all these phytonutrients have consistently shown great potential as preventives and therapeutics on many diseases in vitro, in vivo, and clinical studies. Full article