Templated Polymer Replica Nanoparticles to Facilitate Assessment of Material-Dependent Pharmacokinetics and Biodistribution
Bibliographic record
Abstract
Surface modification is frequently used to tailor the interactions of nanoparticles with biological systems. In many cases, the chemical nature of the treatments employed to modify the biological interface (for example attachment of hydrophilic polymers or targeting groups) is the focus of attention. However, isolation of the fundamental effects of the materials employed to modify the interface are often confounded by secondary effects imparted by the underlying substrate. Herein, we demonstrate that polymer replica particles templated from degradable mesoporous silica provide a facile means to evaluate the impact of surface modification on the biological interactions of nanomaterials, independent of the substrate. Poly(ethylene glycol) (PEG), poly(N-(2 hydroxypropyl)methacrylamide) (PHPMA), and poly(methacrylic acid) (PMA) were templated onto mesoporous silica and cross-linked and the residual particles were removed. The resulting nanoparticles, comprising interfacial polymer alone, were then investigated using a range of in vitro and in vivo tests. As expected, the PEG particles showed the best stealth properties, and these trends were consistent in both in vitro and in vivo studies. PMA particles showed the highest cell association in cell lines in vitro and were rapidly taken up by monocytes in ex vivo whole blood, properties consistent with the very high in vivo clearance subsequently seen in rats. In contrast, PHPMA particles showed rapid association with both granulocytes and monocytes in ex vivo whole blood, even though in vivo clearance was less rapid than the PMA particles. Rat studies confirmed better systemic exposure for PEG and PHPMA particles when compared to PMA particles. This study provides a new avenue for investigating material-dependent biological behaviors of polymer particles, irrespective of the properties of the underlying core, and provides insights for the selection of polymer particles for future biological applications.
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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".