The antimicrobial activity of innate host-directed therapies: A systematic review
Bibliographic record
Abstract
Intracellular human pathogens are the deadliest infectious diseases and are difficult to treat effectively due to their protection inside the host cell and the development of antimicrobial resistance (AMR). An emerging approach to combat these intracellular pathogens is host-directed therapies (HDT), which harness the innate immunity of host cells. HDT rely on small molecules to promote host protection mechanisms that ultimately lead to pathogen clearance. These therapies are hypothesized to: (1) possess indirect yet broad, cross-species antimicrobial activity, (2) effectively target drug-resistant pathogens, (3) carry a reduced susceptibility to the development of AMR and (4) have synergistic action with conventional antimicrobials. As the field of HDT expands, this systematic review was conducted to collect a compendium of HDT and their characteristics, such as the host mechanisms affected, the pathogen inhibited, the concentrations investigated and the magnitude of pathogen inhibition. The evidential support for the main four HDT hypotheses was assessed and concluded that HDT demonstrate robust cross-species activity, are active against AMR pathogens, clinical isolates and laboratory-adapted pathogens. However, limited information exists to support the notion that HDT are synergistic with canonical antimicrobials and are less predisposed to AMR development.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".