Transfer-based nuclear magnetic resonance uncovers unique mechanisms for protein–polymer and protein–nanoparticle binding behavior
Bibliographic record
Abstract
Nanoparticle-based drug delivery systems have shown increasing popularity as a means to improve patient outcomes by improving the effectiveness of active pharmaceutical ingredients (APIs). Similarly, nanoparticles have shown success in targeting alternative routes of API administration, such as applying mucoadhesion or mucopenetration to mucosal drug delivery to enhance uptake. While there are many promising examples of mucoadhesive nanomedicines in literature, there are also many examples of contradictory mucoadhesive binding behavior, most prominently in cases using the same nanoparticle materials. We have uncovered mechanistic insights in polymer-protein binding systems using nOe transfer-based NMR and sought to leverage them to explore nanoparticle-protein interactions. We tested several polymer-coated nanoparticles and micellar polymer nanoparticles and evaluated their binding with mucin proteins. We uncovered that the composition and interaction intimacy of polymer moieties that promote mucin binding change when the polymers are incorporated onto nanoparticle surfaces compared to polymer in solution. This change from solution state to nanoparticle coating can enable switching of behavior of these materials from inert to binding, as we observed in polyvinyl pyrrolidone. We also found the nanoparticle core was influential in determining the binding fate of polymer materials, whereas the nanoparticle size did not possess a clear correlation in the ranges we tested (60-270 nm). These experiments demonstrate that identical polymers may switch their binding behavior to mucin as a function of conformational changes that are induced by incorporating the polymers onto the surface of nanoparticles. These NMR-derived insights could be further leveraged to optimize nanoparticle formulations and guide polymer-mediated mucoadhesion.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".