Evaluation of mesoporous silica nanoparticles for oral drug delivery – current status and perspective of MSNs drug carriers
Bibliographic record
Abstract
The oral pathway is considered as the most common method for drug administration, although many drugs, especially the highly pH- and/or enzymatic biodegradable peptide drugs, are very difficult to formulate and achieve a good intestinal absorption. Efficient systematic absorption of an active substance, delivered via oral ingestion, is only achievable if the drug (1) is substantially present as a solution in the gastrointestinal tract, (2) is able to penetrate through the intestinal mucus, (3) overcomes the different gastrointestinal barriers, and (4) provides an effective therapeutic dose. Therefore, optimization of oral bioavailability of poorly-soluble drugs still remains a significant challenge for the pharmaceutical industry. Even though numerous conventional drug carriers have successfully solved some of the issues related to oral delivery of poorly-soluble drugs, only few of them met commercialization requirements. These drawbacks have led the scientific world to reconsider its approaches toward targeted drug delivery systems and researchers started looking for alternative vectorized carriers. In this area, nanoparticle-based materials have several significant advantages over free and non-formulated drugs. For example, nanosized porous silica carriers allow for more sustained and controlled drug release or improved oral bioavailability. Thus, in the present review, we will highlight the most important features of nanostructured silica drug carriers, such as particle size, particle shape, surface roughness or surface functionalization, and underline the key advantages of these nanosupports. In particular, this article will discuss recent progress and challenges in the area of mesoporous silica nanocarriers used for oral drug delivery. Additional emphasis will be set on the biological and chemical features of the gastrointestinal tract as well as currently tested nanoformulations and strategies to avoid drug degradation in the gastrointestinal environment.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".