The Antibacterial Activity of Silicone-Polyether Surfactants
Bibliographic record
Abstract
The increase in microbial resistance to antibiotics underscores the need for novel antibacterial surfaces, particularly for silicone-based implants, because the hydrophobicity of silicones has been linked to undesirable microbial adhesion and biofilm formation. Unfortunately, current strategies for mitigation, such as pretreatment of surfaces with antiseptics/antibiotics, are not consistently effective. In fact, they can facilitate the prevalence of resistant pathogens by exposing bacteria to sublethal concentrations of biocides. Therefore, scientific interest has shifted to preventing initial adhesion (prior to surface colonization) by using surfactants as surface modifiers. Accordingly, Chapter 2 studied the bioactivity of ACR-008 UP (an acrylic-terminated superwetting silicone surfactant) after it was copolymerized in increasing weight percentages with butyl methacrylate (BMA) and/or methyl methacrylate (MMA). Interestingly, copolymers of 20 wt % ACR showed at least 3x less adhesion by Escherichia coli BL21 (E. coli) than any other formulation. This was not a consequence of wettability, which followed a parabolic function with ACR concentration: high contact angles (CA) with sessile water drops were observed at both low (< 20 wt %) and high (> 80 wt %) concentrations of ACR in materials. The CA at 20 wt % ACR was 66°. The lack of E. coli adhesion was ascribed to surfactant-membrane interactions; hence, the antibacterial potential of compounds related to ACR was further probed. Chapter 3, therefore, examines the structure-activity relationships of nonionic silicone polyether surfactants in solution. Azide/alkyne click chemistry was used to prepare a series of eight compounds with consistent hydrophilic tails (8- 44 poly(ethylene glycol) units), but variable hydrophobic heads (branched silicones with 3-10 siloxane linkages, and in two cases phenyl substitutions). The compounds were tested for toxicity at 0.001 w/v %, 2.5 w/v % and their critical micelle concentrations (CMCs), against different concentrations of E. coli in a 3-step assay. Surfactants with smaller head groups had as much as 4x the bioactivity of larger analogues, with the smallest hydrophobe exhibiting potency equivalent to SDS. Smaller PEG chains were similarly associated with higher potency. This data suggests that lower micelle stability, and the theoretically enhanced permeability of smaller silicone head groups in membranes, is linked to antibacterial activity. The results further demonstrate that the simple manipulation of nonionic silicone polyether structures, leads to significant changes in antibacterial action. To ensure similar results were achievable when such surfactants are immobilized on surfaces, 8 compounds with shorter, ethoxysilylpropyl-terminated PEG chains, and branched or linear hydrophobes, were incorporated into a homemade, room temperature vulcanization (RTV) silicone (Chapter 4). The materials, containing 0- 20 wt% surfactants) were then tested for contact killing and cytophobicity against the same E. coli strain. Elastomers modified with 0.5- 1 wt% of (EtO)3Si-PEG- laurate, and separately (EtO)3Si-PEG-tBS, were on average 2x more hydrophilic relative to controls (103°) and differed in their wettability by ~40°, yet both were anti-adhesive; a ~30-fold reduction in adhesion was seen on modified surfaces relative to the control PDMS. Additionally, the (EtO)3Si-PEG-tBS surface demonstrated biocidal behavior, which further highlighted the importance of surfactant chemistry- not just wettability- in observing a specific antibacterial response (if any). Based on the data collated from each Chapter, silicone surfactants seem to have great potential as bioactive agents and warrant further systematic investigations into their mechanisms of action. In so doing, their chemistry may be optimized against different microbes for a variety of applications. In particular, their potential to create non-toxic, cytophobic silicones is particularly encouraging, given the need for anti-adhesive, biofilm preventing material surfaces.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".