<i>Galleria mellonella</i> as a Novel In Vivo Model to Screen Natural Product-Derived Modulators of Innate Immunity

Immunomodulators are drugs that either stimulate or suppress the immune system in response to an immunopathological disease or cancer. The majority of clinically approved immunomodulators are either chemically synthesised (e.g., dexamethasone) or protein-based (e.g., monoclonal antibodies), whose us...

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Bibliographic Details
Main Authors: Claire Louise Wright, Owen Kavanagh
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/12/13/6587
Description
Summary:Immunomodulators are drugs that either stimulate or suppress the immune system in response to an immunopathological disease or cancer. The majority of clinically approved immunomodulators are either chemically synthesised (e.g., dexamethasone) or protein-based (e.g., monoclonal antibodies), whose uses are limited due to toxicity issues, poor bioavailability, or prohibitive cost. Nature is an excellent source of novel compounds, as it is estimated that almost half of all licenced medicines are derived from nature or inspired by natural product (NP) structures. The clinical success of the fungal-derived immunosuppressant cyclosporin A demonstrates the potential of natural products as immunomodulators. Conventionally, the screening of NP molecules for immunomodulation is performed in small animal models; however, there is a growing impetus to replace animal models with more ethical alternatives. One novel approach is the use of <i>Galleria melonella</i> larvae as an in vivo model of immunity. Despite lacking adaptive antigen-specific immunity, this insect possesses an innate immune system comparable to mammals. In this review, we will describe studies that have used this alternative in vivo model to assess the immunomodulating activity of synthetic and NP-derived compounds, outline the array of bioassays employed, and suggest strategies to enhance the use of this model in future research.
ISSN:2076-3417