Mechanisms of action of microbicides commonly used in infection prevention and control
Bibliographic record
Abstract
SUMMARYUnderstanding how commonly used chemical microbicides affect pathogenic microorganisms is important for formulation of microbicides. This review focuses on the mechanism(s) of action of chemical microbicides commonly used in infection prevention and control. Contrary to the typical site-specific mode of action of antibiotics, microbicides often act via multiple targets, causing rapid and irreversible damage to microbes. In the case of viruses, the envelope or protein capsid is usually the primary structural target, resulting in loss of envelope integrity or denaturation of proteins in the capsid, causing loss of the receptor-binding domain for host cell receptors, and/or breakdown of other viral proteins or nucleic acids. However, for certain virucidal microbicides, the nucleic acid may be a significant site of action. The region of primary damage to the protein or nucleic acid is site-specific and may vary with the virus type. Due to their greater complexity and metabolism, bacteria and fungi offer more targets. The rapid and irreversible damage to microbes may result from solubilization of lipid components and denaturation of enzymes involved in the transport of nutrients. Formulation of microbicidal actives that attack multiple sites on microbes, or control of the pH, addition of preservatives or potentiators, and so on, can increase the spectrum of action against pathogens and reduce both the concentrations and times needed to achieve microbicidal activity against the target pathogens.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".