Non-PEGylated liposomes for convection-enhanced delivery of topotecan and gadodiamide in malignant glioma: initial experience
Bibliographic record
Abstract
Convection-enhanced delivery (CED) of highly stable PEGylated liposomes encapsulating chemotherapeutic drugs has previously been effective against malignant glioma xenografts. We have developed a novel, convectable non-PEGylated liposomal formulation that can be used to encapsulate both the topoisomerase I inhibitor topotecan (topoCED) and paramagnetic gadodiamide (gadoCED), providing an ideal basis for real-time monitoring of drug distribution. Tissue retention of topoCED following single CED administration was significantly improved relative to free topotecan. At a dose of 10 microg (0.5 mg/ml), topoCED had a half-life in brain of approximately 1 day and increased the area under the concentration-time curve (AUC) by 28-fold over free topotecan (153.8 vs. 5.5 microg day/g). The combination of topoCED and gadoCED was found to co-convect well in both naïve rat brain and malignant glioma xenografts (correlation coefficients 0.97-0.99). In a U87MG cell assay, the 50% inhibitory concentration (IC(50)) of topoCED was approximately 0.8 microM at 48 and 72 h; its concentration-time curves were similar to free topotecan and unaffected by gadoCED. In a U87MG intracranial rat xenograft model, a two-dose CED regimen of topoCED co-infused with gadoCED greatly increased median overall survival at dose levels of 0.5 mg/ml (29.5 days) and 1.0 mg/ml (33.0 days) vs. control (20.0 days; P < 0.0001 for both comparisons). TopoCED at higher concentrations (1.6 mg/ml) co-infused with gadoCED showed no evidence of histopathological changes attributable to either agent. The positive results of tissue pharmacokinetics, co-convection, cytotoxicity, efficacy, and lack of toxicity of topoCED in a clinically meaningful dose range, combined with an ideal matched-liposome paramagnetic agent, gadoCED, implicates further clinical applications of this therapy in the treatment of malignant glioma.
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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) | 0.001 | 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".