Addressing a future pandemic: how can non-biological complex drugs prepare us for antimicrobial resistance threats?
Bibliographic record
Abstract
Loss of effective antibiotics through antimicrobial resistance (AMR) is one of the greatest threats to human health. By 2050, the annual death rate resulting from AMR infections is predicted to have climbed from 1.27 million per annum in 2019, up to 10 million per annum. It is therefore imperative to preserve the effectiveness of both existing and future antibiotics, such that they continue to save lives. One way to conserve the use of existing antibiotics and build further contingency against resistant strains is to develop alternatives. Non-biological complex drugs (NBCDs) are an emerging class of therapeutics that show multi-mechanistic antimicrobial activity and hold great promise as next generation antimicrobial agents. We critically outline the focal advancements for each key material class, including antimicrobial polymer materials, carbon nanomaterials, and inorganic nanomaterials, and highlight the potential for the development of antimicrobial resistance against each class. Finally, we outline remaining challenges for their clinical translation, including the need for specific regulatory pathways to be established in order to allow for more efficient clinical approval and adoption of these new technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".