Synthesis, Biological Evaluation, and Molecular Docking Study of 3-Amino and 3-Hydroxy-seco A Derivatives of α-Amyrin and 3-Epilupeol as Inhibitors of COX-2 Activity and NF-kB Activation

Autophagy activation alleviates the LPS-induced inflammatory response in endometrial epithelial cells in dairy cows

PROBLEM

Endometritis is a common disease that affects dairy cow reproduction. Autophagy plays a vital role in cellular homeostasis and modulates inflammation by regulating interactions with innate immune signaling pathways. However, little is known about the regulatory relationship between autophagy and inflammation in bovine endometrial epithelial cells (BEECs). Thus, we aimed to determine the role of autophagy in the inflammatory response in BEECs.

METHODS OF STUDY

In the present study, the expression levels of proinflammatory cytokines were measured by quantitative real-time polymerase chain reaction. Changes in the nuclear factor-κB (NF-κB) pathway and autophagy were determined using immunoblotting and immunocytochemistry. The induction of autophagosome formation was visualized by transmission electron microscopy.

RESULTS

Our results demonstrated that autophagy activation was inhibited in LPS-treated BEECs, while activation of the NF-κB pathway and the mRNA expression of IL-6, IL-8, and TNF-α were increased. Furthermore, blocking autophagy with the inhibitor chloroquine increased NF-κB signaling pathway activation and proinflammatory factor expression in LPS-treated BEECs. Conversely, activation of autophagy with the agonist rapamycin inhibited the NF-κB signaling pathway and downregulated proinflammatory factors.

CONCLUSIONS

These data indicated that LPS-induced inflammation was related to the inhibition of autophagy in BEECs. Thus, the activation of autophagy may represent a novel therapeutic strategy for eliminating inflammation in BEECs.

Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Cyclooxygenase-2 (COX-2) inhibitors are nonsteroidal anti-inflammatory drugs that treat inflammation, pain and fever. This study determined the interaction mechanisms of COX-2 inhibitors and the molecular properties needed to design new drug candidates. Using machine learning and explainable AI methods, the inhibition activity of 1488 molecules was modelled, and essential properties were identified. These properties included aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. They affected the water solubility, hydrophobicity and binding affinity of COX-2 inhibitors. The binding mode, stability and ADME properties of 16 ligands bound to the Cyclooxygenase active site of COX-2 were investigated by molecular docking, molecular dynamics simulation and MM-GBSA analysis. The results showed that ligand 339,222 was the most stable and effective COX-2 inhibitor. It inhibited prostaglandin synthesis by disrupting the protein conformation of COX-2. It had good ADME properties and high clinical potential. This study demonstrated the potential of machine learning and bioinformatics methods in discovering COX-2 inhibitors.

Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms

Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase (mPGES-1) are two key targets in anti-inflammatory therapy. Medicine and food homology (MFH) substances have both edible and medicinal properties, providing a valuable resource for the development of novel, safe, and efficient COX-2 and mPGES-1 inhibitors. In this study, we collected active ingredients from 503 MFH substances and constructed the first comprehensive MFH database containing 27,319 molecules. Subsequently, we performed Murcko scaffold analysis and K-means clustering to deeply analyze the composition of the constructed database and evaluate its structural diversity. Furthermore, we employed four supervised machine learning algorithms, including support vector machine (SVM), random forest (RF), deep neural networks (DNNs), and eXtreme Gradient Boosting (XGBoost), as well as ensemble learning, to establish 640 classification models and 160 regression models for COX-2 and mPGES-1 inhibitors. Among them, ModelA_ensemble_RF_1 emerged as the optimal classification model for COX-2 inhibitors, achieving predicted Matthews correlation coefficient (MCC) values of 0.802 and 0.603 on the test set and external validation set, respectively. ModelC_RDKIT_SVM_2 was identified as the best regression model based on COX-2 inhibitors, with root mean squared error (RMSE) values of 0.419 and 0.513 on the test set and external validation set, respectively. ModelD_ECFP_SVM_4 stood out as the top classification model for mPGES-1 inhibitors, attaining MCC values of 0.832 and 0.584 on the test set and external validation set, respectively. The optimal regression model for mPGES-1 inhibitors, ModelF_3D_SVM_1, exhibited predictive RMSE values of 0.253 and 0.35 on the test set and external validation set, respectively. Finally, we proposed a ligand-based cascade virtual screening strategy, which integrated the well-performing supervised machine learning models with unsupervised learning: the self-organized map (SOM) and molecular scaffold analysis. Using this virtual screening workflow, we discovered 10 potential COX-2 inhibitors and 15 potential mPGES-1 inhibitors from the MFH database. We further verified candidates by molecular docking, investigated the interaction of the candidate molecules upon binding to COX-2 or mPGES-1. The constructed comprehensive MFH database has laid a solid foundation for the further research and utilization of the MFH substances. The series of well-performing machine learning models can be employed to predict the COX-2 and mPGES-1 inhibitory capabilities of unknown compounds, thereby aiding in the discovery of anti-inflammatory medications. The COX-2 and mPGES-1 potential inhibitor molecules identified through the cascade virtual screening approach provide insights and references for the design of highly effective and safe novel anti-inflammatory drugs.

Anti-inflammatory effects of naringenin 8-sulphonate from Parinari excelsa Sabine stem bark and its semi-synthetic derivatives

The inflammatory response is a vital mechanism for repairing damage induced by aberrant health states or external insults; however, persistent activation can be linked to numerous chronic diseases. The nuclear factor kappa β (NF-κB) inflammatory pathway and its associated mediators have emerged as critical targets for therapeutic interventions aimed at modulating inflammation, necessitating ongoing drug development. Previous studies have reported the inhibitory effect of a hydroethanol extract derived from Parinari excelsa Sabine (Chrysobalanaceae) on tumour necrosis factor-alpha (TNF-α), but the phytoconstituents and mechanisms of action remained elusive. The primary objective of this study was to elucidate the phytochemical composition of P. excelsa stem bark and its role in the mechanisms underpinning its biological activity. Two compounds were detected via HPLC-DAD-ESI(Ion Trap)-MS2 analysis. The predominant compound was isolated and identified as naringenin-8-sulphonate (1), while the identity of the second compound (compound 2) could not be determined. Both compound 1 and the extract were assessed for anti-inflammatory properties using a cell-based inflammation model, in which THP-1-derived macrophages were stimulated with LPS to examine the treatments' effects on various stages of the NF-κB pathway. Compound 1, whose biological activity is reported here for the first time, demonstrated inhibition of NF-κB activity, reduction in interleukin 6 (IL-6), TNF-α, and interleukin 1 beta (IL-1β) production, as well as a decrease in p65 nuclear translocation in THP-1 cells, thus highlighting the potential role of sulphur substituents in the activity of naringenin (3). To explore the influence of sulphation on the anti-inflammatory properties of naringenin derivatives, we synthesized naringenin-4'-O-sulphate (4) and naringenin-7-O-sulphate (5) and evaluated their anti-inflammatory effects. Naringenin derivatives 4 and 5 did not display potent anti-inflammatory activities; however, compound 4 reduced IL-1β production, and compound 5 diminished p65 translocation, with both exhibiting the capacity to inhibit TNF-α and IL-6 production. Collectively, the findings demonstrated that the P. excelsa extract was more efficacious than all tested compounds, while providing insights into the role of sulphation in the anti-inflammatory activity of naringenin derivatives.

Structure and anti-inflammatory activity of neo-clerodane diterpenoids from Scutellaria barbata

Herein, 13 previously undescribed neo-clerodane diterpenoids (1-13) and 27 known analogs (14-40) were isolated from the aerial parts of Scutellaria barbata. Absolute configurations of undescribed compounds were assigned based on single-crystal X-ray diffraction analysis and comparison of experimental and circular dichroism. All isolates were evaluated for the inhibition of nitric oxide generation induced by lipopolysaccharide in RAW 264.7 macrophages. Compound 36 was found to be the most active with an IC₅₀ value of 10.6 μM. Structure-activity relations of these neo-clerodane diterpenoids revealed that the α, β-unsaturated-γ-lactone moiety with an exocyclic conjugated double bond was necessary for maintaining and increasing its activity. Further mechanistic studies show that compound 36 suppressed nitric oxide synthase enzymes (iNOS) expression without affecting iNOS activity. Additionally, compound 36 suppresses NF-κB signaling by inhibiting IκBα phosphorylation.

Identification of dietary compounds that interact with the circadian clock machinery: Molecular docking and structural similarity analysis

The molecular clock is vital for regulating circadian rhythms in various physiological processes, and its dysregulation is associated with multiple diseases. As such, the use of small molecule modulators to regulate the molecular clock presents a promising therapeutic approach. In this study, we generated a homology model of the human circadian locomotor output cycles kaput (CLOCK) protein to evaluate its ligand binding sites. Using molecular docking, we obtained further insights into the binding mode of the control compound CLK8 and explored a selection of dietary compounds. Our investigation of dietary compounds was guided by their potential interactions with the retinoic acid-related orphan receptors RORα/γ, which are involved in circadian regulation. Through the molecular similarity and docking analyses, we identified oleanolic acid demethyl, 3-epi-lupeol, and taraxasterol as potential ROR-interacting compounds. These compounds may exert therapeutic effects through their modulation of RORα/γ activity and subsequently influence the molecular clock. Overall, our study highlights the potential of small molecule modulators in regulating the molecular clock and the importance of exploring dietary compounds as a source of such modulators. Our findings also provide insights into the binding mechanisms of CLK8 and shed light on potential compounds that can interact with RORs to regulate the molecular clock. Future investigations could focus on validating the efficacy of these compounds in modulating the molecular clock and their potential use as therapeutic agents.