This study elaborated the multi-component, multi-target, and multi-pathway interaction mechanism of Eucommia ulmoides-Dipsaci Radix herbal pair in the treatment of osteoporosis.

Rhodamine B (RhB) is a banned food additive and has been classified as illegal colourant. Therefore, the risk of RhB contamination should be strictly monitored. In this study, a sensitive UPLC-MS/MS method was applied to monitor RhB in 292 various spices such as chilli, pepper and tomato products. The results showed 22.7% of chilli powder samples, 18.5% of pepper powder samples, 11.1% of chilli oil samples and 9.1% of pepper oil samples were contaminated with RhB. Chilli powder contained RhB up to 44,935 μg/kg with an average of 743 μg/kg, pepper powder up to 65.9 μg/kg with an average of 4.1 μg/kg, chilli oil up to 14.6 μg/kg with an average of 1.0 μg/kg and pepper oil up to 1.1 μg/kg with an average of 0.2 μg/kg, respectively. Considering the common consumption of chilli products and pepper products by so many consumers, RhB exposure is significant and should be decreased.

Eucommia ulmoides Oliver is one of the commonly used traditional Chinese medicines for the treatment of osteoporosis, and iridoid glycosides are considered to be its active ingredients against osteoporosis. This study aims to clarify the chemical components and molecular mechanism of iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis by integrating network pharmacology and molecular simulations. The active iridoid glycosides and their potential targets were retrieved from text mining as well as Swiss Target Prediction, TargetNet database, and STITCH databases. At the same time, DisGeNET, GeneCards, and Therapeutic Target Database were used to search for the targets associated with osteoporosis. A protein–protein interaction network was built to analyze the interactions between targets. Then, DAVID bioinformatics resources and R 3.6.3 project were used to carry out Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Moreover, interactions between active compounds and potential targets were investigated through molecular docking, molecular dynamic simulation, and binding free energy analysis. The results showed that a total of 12 iridoid glycosides were identified as the active iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis. Among them, aucubin, reptoside, geniposide and ajugoside were the core compounds. The enrichment analysis suggested iridoid glycosides of Eucommia ulmoides Oliver prevented osteoporosis mainly through PI3K-Akt signaling pathway, MAPK signaling pathway and Estrogen signaling pathway. Molecular docking results indicated that the 12 iridoid glycosides had good binding ability with 25 hub target proteins, which played a critical role in the treatment of osteoporosis. Molecular dynamic and molecular mechanics Poisson–Boltzmann surface area results revealed these compounds showed stable binding to the active sites of the target proteins during the simulations. In conclusion, our research demonstrated that iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis involved a multi-component, multi-target and multi-pathway mechanism, which provided new suggestions and theoretical support for treating osteoporosis.

Type 2 diabetes is becoming a global pandemic disease. As an important target for the generation and development of diabetes mellitus, peroxisome proliferator-activated receptor γ (PPARγ) has been widely studied. PPARγ agonists have been designed as potential anti-diabetic agents. The advanced development of PPARγ agonists represents a valuable research tool for diabetes therapy. To explore the structural requirements of PPARγ agonists, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking studies were performed on a series of N-benzylbenzamide derivatives employing comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and surflex-dock techniques. The generated models of CoMFA and CoMSIA exhibited a high cross-validation coefficient (q²) of 0.75 and 0.551, and a non-cross-validation coefficient (r²) of 0.958 and 0.912, respectively. The predictive ability of the models was validated using external validation with predictive factor (r²_pred) of 0.722 and 0.682, respectively. These results indicate that the model has high statistical reliability and good predictive power. The probable binding modes of the best active compounds with PPARγ active site were analyzed, and the residues His323, Tyr473, Ser289 and Ser342 were found to have hydrogen bond interactions. Based on the analysis of molecular docking results, and the 3D contour maps generated from CoMFA and CoMSIA models, the key structural features of PPARγ agonists responsible for biological activity could be determined, and several new molecules, with potentially higher predicted activity, were designed thereafter. This work may provide valuable information in further optimization of N-benzylbenzamide derivatives as PPARγ agonists.