Ion exchange biomaterials to capture daptomycin and prevent resistance evolution in off-target bacterial populations
Bibliographic record
Abstract
Abstract Daptomycin (DAP), a cyclic anionic lipopeptide antibiotic, is among the last resorts to treat multidrug resistant (vancomycin resistant Enterococcus faecium or methicillin resistant Staphylococcus aureus ) Gram-positive bacterial infections. DAP is administered intravenously and biliary excretion results in the introduction of DAP (∼5-10 % of the intravenous DAP dose) arriving in the gastrointestinal (GI) tract where it drives resistance evolution in off-target populations of Enterococcus faecium bacteria. Previously, we have shown that the oral administration of cholestyramine, an ion exchange biomaterial (IXB) sorbent, prevents DAP treatment from enriching DAP-resistance in populations of E. faecium shed from mice. Here, we engineer the biomaterial-DAP interfacial interactions to uncover the antibiotic removal mechanisms. The IXB-mediated DAP capture from aqueous media was measured in both controlled pH/electrolyte solutions and in simulated intestinal fluid (SIF) to uncover the molecular and colloidal mechanisms of DAP removal from the GI tract. Our findings show that the IXB electrostatically adsorbs the anionic antibiotic via a time-dependent diffusion-controlled process. Unsteady-state diffusion-adsorption mass balance describes the dynamics of adsorption well, and the maximum removal capacity is beyond the electric charge stoichiometric ratio because of DAP self-assembly. This study may open new opportunities for optimizing cholestyramine adjuvant therapy to prevent DAP resistance, as well as designing novel biomaterials to remove off-target antibiotics from the GI tract. TOC
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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) | 0.001 | 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".