Triggered Release from Thermosensitive Liposomes Improves Tumor Targeting of Vinorelbine
Bibliographic record
Abstract
Abstract Triggered drug delivery strategies have been shown to enhance drug accumulation at target diseased sites in comparison to administration of free drug. In particular, many studies have demonstrated improved targetability of chemotherapeutics when delivered via thermosensitive liposomes. However, most studies continue to focus on encapsulating doxorubicin while many other drugs would benefit from this targeted and localized delivery approach. The proposed study explores the therapeutic potential of a thermosensitive liposome formulation of the commonly used chemotherapy drug vinorelbine in combination with mild hyperthermia (39-43 °C) in a murine model of rhabdomyosarcoma. Rhabdomyosarcoma, the most common soft tissue sarcoma in children, is largely treated using conventional chemotherapy which is associated with significant adverse long-term sequelae. In this study, mild hyperthermia was pursued as a non-invasive, non-toxic means to improve the efficacy and safety profiles of vinorelbine. Thorough assessment of the pharmacokinetics, biodistribution, efficacy and toxicity of vinorelbine administered in the thermosensitive liposome formulation was compared to administration in a traditional, non-thermosensitive liposome formulation. This study shows the potential of an advanced formulation technology in combination with mild hyperthermia as a means to target an untargeted therapeutic agent and result in a significant improvement in its therapeutic index. Graphical abstract
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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) | 0.003 | 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".