Surface Presentation of Hyaluronic Acid Modulates Nanoparticle–Cell Association
Bibliographic record
Abstract
Nanoparticle (NP) drug carriers have revolutionized medicine and increased patient quality of life. Clinically approved formulations typically succeed because of reduced off-target toxicity of the cargo. However, increasing carrier accumulation at disease sites through precise targeting remains one of the biggest challenges in the field. Novel multivalent ligand presentations and self-assembled constructs can enhance cell association, but an inability to draw direct comparisons across formulations has hindered progress. Furthermore, how nanoparticle structure influences function often is unclear. In this report, we leverage the well-characterized hyaluronic acid (HA)-CD44 binding pair to investigate how the surface architecture of modified NPs impacts their association with ovarian cancer cells that overexpress CD44. We functionalized anionic liposomes with 5 kDa HA by either covalent conjugation via surface coupling or electrostatic self-assembly using the layer-by-layer (LbL) adsorption method. Comparing these two methods, we observed a consistent enhancement of NP-cell association with the self-assembly LbL technique, particularly with higher molecular weight (≥10 kDa) HA. To further optimize association, we increased the surface-available HA. We synthesized a bottlebrush glycopolymer composed of a polynorbornene backbone and pendant 5 kDa HA and layered this macromolecule onto NPs. Flow cytometry revealed that the LbL HA bottlebrush NP outperformed the LbL linear display of HA. Cellular visualization by deconvolution optical microscopy corroborated results from all three constructs. Using exogenous HA to block NP-CD44 interactions, we found the LbL HA bottlebrush NP had a 4-fold higher binding avidity than the best-performing LbL linear HA NP. We further observed that decreasing the density of HA bottlebrush side chains to 75% had minimal impact on LbL NP stability or cell association, though we did see a reduction in binding avidity with this side-chain-modified NP. Our studies indicate that LbL surfaces are highly effective for multivalent displays, and the mode in which they present a targeting ligand can be optimized for NP cell targeting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".