This Research Topic is part of a series of: https://www.frontiersin.org/research-topics/36328/the-application-of-network-analysis-in-ethnopharmacology
Medicinal Plants (MPs) and Plant-based Foods (PFs) remain important to prevent and prevent or treat many diseases . MPs and PFs are rich in diverse phytochemical compounds, such as polyphenols, flavonoids, essential oils, alkaloids, terpenoids, and lignan. However, due to their complex composition, it is still elusive to understand related molecular mechanisms of their beneficial functions, and it is still challenging for MPs to be used with rational phytotherapy or biomedicine. Equally, the concept of ‘ whole food’, like the whole grain, has become an important approach in modern nutrition. Network Analysis is a bioinformatic-based strategy initially used for new drug discovery and has been increasingly applied to ethnopharmacology and food nutrition in recent years. Key challenges in this context include that the target prediction is based on existing experimental data, mostly without assessing the validity of the existing experimental information. Therefore falso positive claims are a serious challenge. This is exacerbated by the common polyvalent (non-specific) effects of many polyphenolic compounds including flavonoids or phenylpropanoids
Network analysis can be used to set up a network of MPs/FPs/Formula-Compounds-Targets-Functions/Diseases. This network can be used to explore potential effective/toxic compounds in MPs/FPs and their prescriptions/formulations, predict the potential molecular targets and molecular mechanisms of them, and design MP prescriptions or FP recipes with certain health benefits. Importantly, a network analysis can, due to ambiguity of the existing scientific literature normally predict one to many associations, like one metabolite to many targets or many metabolites to one target. A ‘many to many’ correlation is prone to result in artefacts. Finally, an experiment-based pharmacological/nutritional assessment is essential following such network analysis-based predictions.
Therefore, this Research Topic aims to bring together the most recent research progress associated with applying network analysis in ethnopharmacology and food nutrition. We encourage the submissions of original research articles, perspectives, opinion articles, and reviews that focus on, but are not limited to, the following potential topics:
• Prediction of potential molecular targets and new pharmacological effects of MPs/FPs and their phytochemical compounds
• Prediction of the toxicity and toxic compounds of MPs/FPs and their prescriptions/formulations
• Assessing the specificity of target prediction of alleged active metabolites known for polyvalent (non-specific) activity
• Precise design of MP prescriptions/functional food or nutraceutical formula with certain health benefits
Important Note:
All contributions to this Research Topic must follow the guideline listed in this section:
• The introduction should describe the background of the research object and provide bibliographical references that illustrate its recent application in general healthcare.
• Research-baseded solely on in silico approaches (e.g., network studies or docking experiments) does not fit with the scopes of this SI.
• Small molecules exhibiting in silico or in vitro effects but without specific pharmacological targets do not fit with the scopes of this SI.
• Chemical anti-oxidant assays like the DPPH or ABTS assay are of no pharmacological relevance, Therefore they can only be used a chemical-analytical assays without pharmacological claims.
• Please self-assess your MS using the ConPhyMP tool (https://ga-online.org/best-practice) and submit the relevant sections of the tool with your submission. You need to follow the standards established in the ConPhyMP statement Front. Pharmacol. 13:953205).
• All the manuscripts need to fully comply with the
Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version
here). Importantly, please ascertain that the ethnopharmacological context is clearly described (pillar 3d) and that the material investigated is characterized in detail (
pillars 2 a and b).