Use of nanoscale delivery systems to maintain synergistic drug ratios<i>in vivo</i>
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Drug combinations have been the standard of care in the treatment of cancer for > 50 years. Typically, combination chemotherapy uses agents with non-overlapping toxicities which are escalated to their maximum tolerated dose. However, emerging evidence indicates that this approach may not be providing optimal efficacy depending on the drug ratios to which the tumor is exposed. Combined drugs can be synergistic whereas other ratios of the same agents may be antagonistic or additive. AREAS COVERED IN THIS REVIEW: In this review, we examine the importance of drug ratios in cancer therapy. We describe how manipulation of the lipid membrane and internal buffer composition maintains synergistic ratios of irinotecan and floxuridine (CPX-1), daunorubicin and cytarabine (CPX-351) or cisplatin and irinotecan (CPX-571). For polymer-based nanoparticles, prodrug hydrophobicity was exploited to coordinate the release of gemcitabine and the more hydrophobic paclitaxel. We present preclinical data for liposomal drug combinations which demonstrate that the most efficacious formulation is not always the highest dose of both agents. WHAT THE READER WILL GAIN: An insight into the use of liposomes and polymer-based nanoparticles to deliver synergistic drug combinations to the tumor site and avoid antagonistic drug-drug interactions. TAKE HOME MESSAGE: The ability to control and maintain drug ratios in vivo through the use of nanoscale delivery vehicles results in a significant improvement in therapeutic activity.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".