Size-Controlled Functionalized Mesoporous Silica Nanoparticles for Tunable Drug Release and Enhanced Anti-Tumoral Activity
Bibliographic record
Abstract
Mesoporous silica nanoparticles (MSNs) are considered as one of the most promising nanovectors for controlled drug delivery. For the design of ideal drug nanocarriers, several factors have to be taken into account, such as size and surface chemistry. Here, we report how MSNs surface functionalization and particle size critically affect the drug release performances and therapeutic capabilities. We illustrate the size effect of these functionalized MSNs on in vitro, intracellular, and in vivo drug release efficiency, as well as on nanoparticle and drug diffusion into the targeted tissues (tumor). For this, dispersible MSNs with different particle sizes (from 500 down to 45 nm), similar physicochemical properties (e.g., structural and textural properties), and high colloidal stability (even in saline conditions), were synthesized. Their surface was specifically functionalized with a phosphonate-silane according to a novel postgrafting strategy, for better control over loading and release of positively charged drugs. An efficient particle-size-dependent and pH-dependent release of the loaded drug (i.e., doxorubicin) was achieved in physiological conditions with phosphonated-MSNs compared to pure-MSNs. The cellular uptake efficiency is much higher with the smallest phosphonated-nanoparticles (45 nm). Furthermore, doxorubicin is efficiently released from the nanoparticles into the intracellular compartments, and the drug reaches the nucleus in a time- and particle size-dependent manner. Intratumoral diffusion of the developed nanoparticles, as well as the drug release and its diffusion into the tumor matrix, is clearly enhanced with the smallest phosphonated-nanoparticles (45 nm), leading ultimately to a superior cell and tumor growth inhibition.
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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".