Mesoporous Silica Nanoparticles: Selective Surface Functionalization for Optimal Relaxometric and Drug Loading Performances
Bibliographic record
Abstract
Mesoporous silica nanoparticles (MSNs) have emerged as promising biomaterials for drug delivery and cell tracking applications, for which MRI is the medical imaging modality of choice. In this contribution, MRI contrast agents (DTPA‐Gd) and polyethylene glycol (PEG) are grafted selectively at the surface of MSNs, in order to achieve optimal relaxometric and drug loading performances. In fact, DTPA and PEG grafting procedures reported until now, have resulted in significant pore obstruction, which is detrimental to the drug delivery function of MSNs. This usually induces a dramatic decrease in surface area and pore volume, thus limiting drug loading capacity. Therefore, these molecules must be selectively grafted at the outer surface of MSNs. In this study, 3D pore network MSNs (MCM‐48‐type) are synthesized and functionalized with a straightforward and efficient grafting procedure in which DTPA and PEG are selectively grafted at the outer surface of MSNs. No pore blocking is observed, and more than 90% of surface area, pore volume and pore diameter are retained. The thus‐treated particles are colloidally stable in SBF and cell culture media, they are not cytotoxic and they have high drug loading capacity. Upon labeling with Gd, the nanoparticle suspensions have strong relaxometric properties (r 2 /r 1 = 1.47, r 1 = 23.97 mM −1 s −1 ), which confers a remarkable positive contrast enhancement potential to the compound. The particles could serve as efficient drug carriers, as demonstrated with a model of daunorubicin submitted to physiological conditions. The selective nanoparticle surface grafting procedures described in the present article represent a significant advance in the design of high colloidal stability silica‐based vectors with high drug loading capacity, which could provide novel theranostic nanocompounds.
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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) | 0.000 | 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".