About this Research Topic
Herbal medicines and individual metabolites isolated from them offer multiple opportunities in the treatment and management of infectious diseases , which in recent has years become increasingly important. For example, new treatment options are needed in the face of increasing antimicrobial resistance and emerging viral threats. Infectious diseases such as COVID-19, influenza, dengue, Zika, hepatitis B and C, Ebola, monkeypox, and the herpes simplex virus (HSV) continue to pose a major challenge to global health. Traditionally, medicines derived from plants, microorganisms, and marine sources have provided a rich source of pharmacologically active compounds. The rapid emergence of drug resistance and mutations seen in various viral infections has created an urgent need for novel treatments. Natural products have long been a valuable resource in drug discovery as they offer complex chemical structures with high bioactivity. However, traditional drug discovery methods are time-consuming and labour-intensive.
Computational approaches such as molecular docking, virtual screening, and machine learning offer opportunities in drug discovery process by identifying potential drug candidates faster and more efficiently. However, the current in silico methods used have multiple methodological and conceptual flaws, which often result in meaningless outcomes. For example, false positive or false negative outcomes of previous studies impact on all approaches using the evidence based on the existing scientific literature, and must be taken into account. Many compounds including for example polyphenols shown non-specific, therapeutically non-relevant effects in in silico and in vitro studies. As a consequence purely computational studies are generally misleading, especially if they are used with complex mixtures like plant extracts and a combination of experimental and in silico approaches offers new opportunities
These combinations of computational and pharmacological strategies have the potential assist with scientific developments in the diverse field of infectious diseases including viral infections and to overcome drug resistance mechanisms. This area of research plays a crucial role in the ongoing global effort to develop effective treatments for a variety of infectious diseases.
The main objective of this research topic is to identify and develop plant and fungal metabolites and extracts with potent therapeutic activity against infectious diseases, including the conditions listed above.
Recent advances in computational approaches such as molecular docking, pharmacophore modelling, and machine learning offer novel in silico approaches. These methods enable the rapid but preliminary screening of vast chemical libraries derived from natural sources and identify potential drug candidates. However, there are multiple conceptual and methodological problems which need to be overcome and purely in silico approaches most notably with extracts mostly result in non-usable results. Combining these computational techniques with pharmacological in vitro or in vivo can improve the discovery process of antiviral agents with novel mechanisms of action. In addition, advances in structural biology, omics technologies, and in silico modelling provide deeper insights into the molecular interactions between metabolites, which are already known to show activity and viral targets. This integrative approach has the potential to accelerate the development of safe, effective, and affordable therapies for infectious diseases.
Scope of the Research Topic:
1. Combined computational and pharmacological approaches to natural drug discovery for infectious diseases, including but not limited to COVID-19, influenza (flu), dengue virus, Zika virus, hepatitis B and C viruses, Ebola virus, monkeypox, and herpes simplex virus (HSV).
2. Identification, characterization, and optimization of medicinal plant and fungal extracts for infectious diseases.
3. Preclinical and clinical studies of natural medicines for infectious diseases.
4. Combination therapies with natural medicines for infectious diseases.
5. Identification of novel leads derived from plants and fungi to treat infectious diseases
6. Improved drug delivery and formulation approaches for natural medicines.
Important Note:
All contributions to this Research Topic must follow the guideline listed in this section:
• Purely in silico/AI-based studies are outside of our scope.
• The introduction needs to describe the background of the research object focusing on the traditional or local use of a traditional medicine and provide bibliographical references that illustrate its recent application in general healthcare.
• Network studies must critically assess the pharmacological evidence to evaluate the potential effects of a preparation / herbal (medical) product and the limitations of the evidence. An in vitro or in vivo assessment needs to be an integrated part of the study
• 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).
Computational approaches such as molecular docking, virtual screening, and machine learning offer opportunities in drug discovery process by identifying potential drug candidates faster and more efficiently. However, the current in silico methods used have multiple methodological and conceptual flaws, which often result in meaningless outcomes. For example, false positive or false negative outcomes of previous studies impact on all approaches using the evidence based on the existing scientific literature, and must be taken into account. Many compounds including for example polyphenols shown non-specific, therapeutically non-relevant effects in in silico and in vitro studies. As a consequence purely computational studies are generally misleading, especially if they are used with complex mixtures like plant extracts and a combination of experimental and in silico approaches offers new opportunities
These combinations of computational and pharmacological strategies have the potential assist with scientific developments in the diverse field of infectious diseases including viral infections and to overcome drug resistance mechanisms. This area of research plays a crucial role in the ongoing global effort to develop effective treatments for a variety of infectious diseases.
The main objective of this research topic is to identify and develop plant and fungal metabolites and extracts with potent therapeutic activity against infectious diseases, including the conditions listed above.
Recent advances in computational approaches such as molecular docking, pharmacophore modelling, and machine learning offer novel in silico approaches. These methods enable the rapid but preliminary screening of vast chemical libraries derived from natural sources and identify potential drug candidates. However, there are multiple conceptual and methodological problems which need to be overcome and purely in silico approaches most notably with extracts mostly result in non-usable results. Combining these computational techniques with pharmacological in vitro or in vivo can improve the discovery process of antiviral agents with novel mechanisms of action. In addition, advances in structural biology, omics technologies, and in silico modelling provide deeper insights into the molecular interactions between metabolites, which are already known to show activity and viral targets. This integrative approach has the potential to accelerate the development of safe, effective, and affordable therapies for infectious diseases.
Scope of the Research Topic:
1. Combined computational and pharmacological approaches to natural drug discovery for infectious diseases, including but not limited to COVID-19, influenza (flu), dengue virus, Zika virus, hepatitis B and C viruses, Ebola virus, monkeypox, and herpes simplex virus (HSV).
2. Identification, characterization, and optimization of medicinal plant and fungal extracts for infectious diseases.
3. Preclinical and clinical studies of natural medicines for infectious diseases.
4. Combination therapies with natural medicines for infectious diseases.
5. Identification of novel leads derived from plants and fungi to treat infectious diseases
6. Improved drug delivery and formulation approaches for natural medicines.
Important Note:
All contributions to this Research Topic must follow the guideline listed in this section:
• Purely in silico/AI-based studies are outside of our scope.
• The introduction needs to describe the background of the research object focusing on the traditional or local use of a traditional medicine and provide bibliographical references that illustrate its recent application in general healthcare.
• Network studies must critically assess the pharmacological evidence to evaluate the potential effects of a preparation / herbal (medical) product and the limitations of the evidence. An in vitro or in vivo assessment needs to be an integrated part of the study
• 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).
Keywords: Infectious diseases, Natural compounds, Artificial intelligence, Bioinformatics (computational biopharmaceutics and modeling), Pharmaceutical Chemistry, Network Pharmacology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
