In vitro and In vivo Synergistic Interactions of the Flavonoid Rutin with Paracetamol and with Non-Steroidal Anti-Inflammatory Drugs

Synergistic Proliferation Effects of Xanthohumol and Niflumic Acid on Merkel and Glioblastoma Cancer Cells: Role of Cell Membrane Interactions

The objective of our research was to determine the effects of xanthohumol (XN), a flavonoid isolated from hops (Humulus lupulus), and the anti-inflammatory drug niflumic acid (NA), separately and in combination with each other, on the proliferation of human cancer cells. Additionally, so as to understand the mechanism underlying the anticancer properties of the tested compounds, their effects on the biophysical parameters of a model membrane were assessed. The cells were incubated with XN and NA at various concentrations, either individually or in combination with each other. Cell proliferation was quantified using the sulforodamine B (SRB) assay. In addition, the IC50 values for niflumic acid and xanthohumol applied separately were determined by cell proliferation tests for the following human cancer cell lines: 5637 (urinary bladder carcinoma), A-431 (epidermoid carcinoma), UM-SCC-17A (head and neck squamous carcinoma), SK-MEL-3 (melanoma), MCC13 (Merkel cell cancer), and A172 (glioblastoma), in comparison with the mouse normal fibroblasts (BALB/3T3 clone A31). The results show that the two-compound combinations of XN and NA significantly decreased the proliferation of cancer cells in a dose-dependent manner, and the effects were stronger than the additive responses to XN and NA individually. The membrane studies revealed a synergistic effect on the membrane rigidity when using the mixture of XN and NA, which may explain the observed increase in anticancer activity for the combined XN and NA. Our results suggest that NSAIDs, such as niflumic acid, may be a promising strategy for co-application with xanthohumol as anticancer drugs.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.

Optimization of the Multienzyme-Assisted Extraction Procedure of Bioactive Compounds Extracts from Common Buckwheat (Fagopyrum esculentum M.) and Evaluation of Obtained Extracts

Optimization of the extraction procedure using a multienzymes cocktail for common buckwheat (Fagopyrum esculentum M.) is important due to the yield, fermentable sugars, oligosaccharides and bioactive compounds for creating higher added value products. This study was undertaken to find out the optimum multienzymes-water extraction on yield and total phenolic compounds for common Buckwheat using response surface methodology (RSM). Three independent variables, time (2, 13, and 24 h), temperature (60 °C, 70 °C, 80 °C), and non-starch polysaccharide (NSP) enzymes mixture (0.10, 0.55, and 1.00 mL), were analyzed to optimize the response variables. NSP hydrolyzing enzymes, cellulase, xylanase, and β-glucanase, were produced by Trichoderma reesei. Estimated optimum conditions for F. esculentum were found: time-2 h, temperature-65 °C, and cellulase activity-8.6 CellG5 Units/mL. Different optimization run samples were collected and lyophilized for further analysis until the hydrophilic property using the water contact angle methodology and rutin content using HPLC was determined. Results indicated NSP enzymes activity did not differ between water contact angles after 13 h of enzymatic water extraction. However, longer fermentation time (24 h) decreased static water contact angle by approximately 3-7° for lyophilized water extract and 2-7° for solid fraction after fermentation. It implies enzymatic hydrolysis during water extraction increased hydrophilic properties in solid fraction and decreased hydrophilicity in water fraction due to the enzymes cleaved glycosidic bonds releasing water-soluble compounds.