Endophytic Fungal Secondary Metabolites as Potential Drug Discovery Agents against Antimicrobial Resistance: A Systematic Review
Bibliographic record
Abstract
Introduction: The rise of antimicrobial resistance has become a pressing global public health concern, necessitating the exploration of alternative antimicrobial agents. Endophytes have been recognized as a rich source of novel metabolites with potential antimicrobial properties. Aim: To comprehensively review the literature on secondary metabolites from endophytic fungi with antimicrobial activities against priority pathogens. Method: A systematic search was conducted in four electronic databases: PubMed, Scopus, Web of Science, and Google Scholar. Studies that reported the isolation and elucidation of secondary endophyte metabolites with inhibitory effects against clinically relevant bacteria and fungi were included. The Newcastle–Ottawa scale (NOS) was used to evaluate the methodological quality of the included studies. Results: Twenty-one studies met the inclusion criteria, and 77 specialised metabolites were characterised across the studies. These secondary metabolites exhibited broad-spectrum in vitro growth inhibition against priority pathogens, including bacteria such as E. coli, Acinetobacter baumannii, and Pseudomonas aeruginosa, and fungi such as Candida albicans and Aspergillus spp. Conclusion: This systematic review provides an overview of the current evidence on secondary endophyte metabolites with antibacterial and antifungal activities. The results highlight the potential of endophytes as a source of new antimicrobial agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".