Advancing the Immunopharmacology of Traditional Medicines: Bioinformatics and Multi-Omics Approaches

Traditional medicine systems worldwide have a long, well-documented history of use in modulating immune function and treating immune-related disorders, yet the molecular mechanisms underlying their multi-target, multi-component actions remain incompletely understood. High-throughput omics technologies — genomics, transcriptomics, proteomics, metabolomics, and single-cell sequencing — combined with bioinformatics and network analysis approaches, offer a means to generate testable hypotheses on how traditionally used medicinal formulas and their documented bioactive compounds may regulate immune pathways. However, resolving the conceptual and methodological ambiguities and developing these computational hypotheses into plausible molecular mechanisms remains a major and largely unresolved challenge for the field.A critical, often overlooked limitation is that in silico predictions — including network analysis and omics-database mining — can at best generate early-stage hypotheses about potential targets; they do not, on their own, constitute evidence of pharmacological activity or molecular mechanism. This challenge is compounded by well-documented data-quality issues in the underlying resources: reported “bioactivities” in many public omics and interaction databases are frequently false positives, an issue closely linked to pan-assay interference compounds (PAINS), which are commonly enriched in crude extracts and multi-component herbal preparations (Bolz et al., 2021, J. Chem. Inf. Model. 10.1021/acs.jcim.0c01227). Metabolites and extracts whose reported activity rests largely on in vitro assay data cannot be assumed to be of pharmacological or clinical relevance without further corroboration.Most existing studies in this space remain fragmented — focusing on a single omics layer, relying on in silico prediction alone, or lacking a clear link between computational findings and the traditional therapeutic rationale of the material studied. This Research Topic aims to fill this gap by bringing together rigorous, traditionally grounded research that critically integrates multi-omics and network analysis with experimental or clinical assessment to elucidate immunopharmacological mechanisms across traditional medicine systems worldwide — explicitly treating computational findings as hypotheses to be tested, not conclusions in themselves.All submissions must provide a short explanation of the traditional use of the medicinal formula(s) or compound(s) under study in the Introduction, and must characterize the study material (crude extract, multi-herbal preparation, or single compound; source, extraction method, and chemical/botanical composition) following established taxonomic standards (e.g., the Kew Medicinal Plant Names Services portal, or an equivalent authoritative source for fungi- or animal-derived materials).Scope — we welcome Original Research, Review, Mini Review, Systematic Review, and Clinical Trial articles addressing (but not limited to):Multi-omics and network analysis approaches to dissect the multi-target immunomodulatory activity of traditionally used medicinal formulas and their documented bioactive compounds. Wet-lab or in vivo confirmation is required to complement computational findings; purely predictive studies without experimental or clinical assessment fall outside the scope of this Research Topic, we invite contributions covering, most importantly, the following themes:Multi-omics investigation of traditionally used medicinal formulas in immune-related diseases (e.g., autoimmune diseases, tumor immune microenvironment, chronic inflammation, infectious diseases), with mechanistic conclusions supported by pharmacological or clinical evidence rather than computational prediction alone.Critical evaluation of data quality and pharmacological relevance in omics- and database-driven traditional medicine research, including approaches to identify and mitigate pan-assay interference compounds (PAINS) and other sources of false-positive bioactivity signals in extracts and complex mixtures.Effects of specific probiotics/microbiome or gut–immune–traditional medicine interactions using multi-omics approaches, with a mechanistic focus on immune modulation validated beyond compositional association.Machine learning/AI-assisted bioinformatics approaches for mining immunopharmacological data of traditionally used medicinal formulas, explicitly framed as hypothesis-generating tools and in combination with experimental (e.g. in vitro) assessments.Studies combining multi-omics findings with in vitro/in vivo immunopharmacological experiments to confirm proposed mechanisms.Development of best-practice frameworks for assessing the pharmacological plausibility of complex mixtures and extracts — including appropriate controls for assay interference and dose-relevance to in vivo/clinical exposure.Databases, computational tools, or methodological frameworks for integrating multi-omics data in traditional medicine research, accompanied by rigorous benchmarking against existing methods and explicit discussion of data-quality limitations.All submissions must comply with the Four Pillars of Best Practice in Ethnopharmacology. Contributions involving plant- or fungus-derived PUMS materials should include detailed descriptions of biological source, extraction methods, and processing procedures. Authors are encouraged to consult the ConPhyMP statement (Front. Pharmacol., 13:953205) and the full best-practice guidance at https://ga-online.org/best-practice, and to self-assess manuscripts using the ConPhyMP tool before submission.Studies involving animal experiments must comply with relevant institutional and national ethical guidelines and report adherence to the 4Rs principles. They need to be relevant in the context of developing evidence-based practice or novel therapeutic approaches for specific and significant improvements in the treatment of major diseases in humans or animals. Studies involving human subjects must include a statement of institutional ethics committee approval.Outside the scope of this Research Topic:Purely in silico studies (network analysis, molecular docking, virtual screening, omics-based target prediction) without experimental or clinical assessment, as such approaches are speculative and cannot establish pharmacological activity or mechanism.Studies reporting only chemical antioxidant assays (e.g., DPPH, ABTS) without mechanistic follow-up relevant to pharmacological activity.Bibliometric or scientometric analyses. Topic editors: Mihong Ren, Yong Li, Mourad Kharbach, Jinxiu Li Source: https://www.frontiersin.org/research-topics/84051/advancing-the-immunopharmacology-of-traditional-medicines-bioinformatics-and-multi-omics-approaches