Heat-activated nanomedicine formulation improves the anticancer potential of the HSP90 inhibitor luminespib in vitro
Bibliographic record
Abstract
The heat shock protein 90 inhibitor, luminespib, has demonstrated potent preclinical activity against numerous cancers. However, clinical translation has been impeded by dose-limiting toxicities that have necessitated dosing schedules which have reduced therapeutic efficacy. As such, luminespib is a prime candidate for reformulation using advanced drug delivery strategies that improve tumor delivery efficiency and limit off-target side effects. Specifically, thermosensitive liposomes are proposed as a drug delivery strategy capable of delivering high concentrations of drug to the tumor in combination with other chemotherapeutic molecules. Indeed, this work establishes that luminespib exhibits synergistic activity in lung cancer in combination with standard of care drugs such as cisplatin and vinorelbine. While our research team has previously developed thermosensitive liposomes containing cisplatin or vinorelbine, this work presents the first liposomal formulation of luminespib. The physico-chemical properties and heat-triggered release of the formulation were characterized. Cytotoxicity assays were used to determine the optimal drug ratios for treatment of luminespib in combination with cisplatin or vinorelbine in non-small cell lung cancer cells. The formulation and drug combination work presented in this paper offer the potential for resuscitation of the clinical prospects of a promising anticancer agent.
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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) | 0.001 | 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".