Insights into the Polyhexamethylene Biguanide (PHMB) Mechanism of Action on Bacterial Membrane and DNA: A Molecular Dynamics Study
Bibliographic record
Abstract
Abstract Polyhexamethylene biguanide (PHMB) is a cationic polymer with antimicrobial and antiviral properties. It has been commonly accepted that the antimicrobial activity is due the ability of PHMB to perforate the bacterial phospholipid membrane leading ultimately to its death. In this study we show by the means of atomistic molecular dynamics (MD) simulations that while the PHMB molecules attach to the surface of the phospholipid bilayer and partially penetrate it, they do not cause any pore formation at least within the microsecond simulation times. The polymers initially adsorb onto the membrane surface via the favourable electrostatic interactions between the phospholipid headgroups and the biguanide groups, and then partially penetrate the membrane slightly disrupting its structure. This, however, does not lead to the formation of any pores. The microsecond-scale simulations reveal that it is unlikely for PHMB to spontaneously pass through the phospholipid membrane. Our findings suggest that PHMB translocation across the bilayer may take place through binding to the phospholipids. Once inside the cell, the polymer can effectively ‘bind’ to DNA through extensive interactions with DNA phosphate backbone, which can potentially block the DNA replication process or activate DNA repair pathways. TOC Graphic
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".