Lipid-based nanoparticles external triggered release strategies in cancer nanomedicine
Bibliographic record
Abstract
Nearly half of humanity will develop cancer in their Lifetime. Current therapies, particularly chemotherapeutic drugs, face significant challenges due to the lack of tissue-specific delivery. For example, less than 0.1% of anticancer drugs administered systemically reach the tumor site, resulting in damage to healthy tissues and leading to a wide range of side effects. An effective strategy to address this problem is the encapsulation of chemotherapeutic drugs within nanoscale synthetic lipid structures, known as lipid-based nanoparticles (LBNPs). LBNPs can enhance a drug's circulation half-life in the bloodstream and exploit the enhanced permeability and retention (EPR) effect. These delivery systems have led to the approval of more than 20 FDA-approved chemotherapeutic drugs. The greatest advantage is often improved pharmacokinetics, which enables a higher maximum tolerated dose while maintaining similar therapeutic efficacy and reducing side effects. However, a key limitation is that in many cases LBNPs are too stable, with free drug released very slowly, which limits anticancer efficacy. Consequently, externally triggered strategies have gained increasing attention, as they allow site-specific and on-demand release of LBNP contents at the tumor, thereby overcoming this stability barrier and enabling higher tumor-specific drug concentrations with fewer systemic side effects. This article reviews recent advances in externally triggered release mechanisms for LBNPs, including thermal, ultrasound, radiation, magnetic, and light-based approaches, and examines their potential integration into clinical cancer settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".