Identification of Apocarotenoids as Chemical Markers of In Vitro Anti-Inflammatory Activity for Spirulina Supplements

Tan, K.; Pham, Tho X.; Lee, Yoojin; Lee, Ji-Young; Balunas, Marcy J.

doi:10.1021/acs.jafc.1c03015

Cited by 4 publications

(1 citation statement)

References 58 publications

(104 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zingiber officinale oil has been reported to possess anti-inflammatory activity, particularly by inhibiting lipoxygenase [26]. cmp_A7, derived from Lycii Fructus, is an apocarotenoid that significantly inhibits the expression of IL-1β in vitro, thus exerting an anti-inflammatory effect [27]. Cmp_A5, named Scutianine C, is derived from in Jujubae Fructus and has been reported as a biologically active alkaloid with antimicrobial properties [28].…”

Section: Potential Cox-2 Inhibitors In the Mfh Databasementioning

confidence: 99%

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

Tian,

Zhang,

Yan

2023

Molecules

View full text Add to dashboard Cite

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.

show abstract

Section: Potential Cox-2 Inhibitors In the Mfh Databasementioning

confidence: 99%