Optimization of liposomal topotecan for use in treating neuroblastoma
Bibliographic record
Abstract
Abstract The purpose of this work was to develop an optimized liposomal formulation of topotecan for use in the treatment of patients with neuroblastoma. Drug exposure time studies were used to determine that topotecan (Hycamtin) exhibited great cytotoxic activity against SK ‐N‐ SH , IMR ‐32 and LAN ‐1 neuroblastoma human cell lines. Sphingomyelin ( SM )/cholesterol (Chol) and 1,2‐distearoyl‐ sn ‐glycero‐3‐phosphocholine ( DSPC )/Chol liposomes were prepared using extrusion methods and then loaded with topotecan by pH gradient and copper‐drug complexation. In vitro studies showed that SM /Chol liposomes retained topotecan significantly better than DSPC /Chol liposomes. Decreasing the drug‐to‐lipid ratio engendered significant increases in drug retention. Dose‐range finding studies on NRG mice indicated that an optimized SM /Chol liposomal formulation of topotecan prepared with a final drug‐to‐lipid ratio of 0.025 (mol: mol) was better tolerated than the previously described DSPC /Chol topotecan formulation. Pharmacokinetic studies showed that the optimized SM /Chol liposomal topotecan exhibited a 10‐fold increase in plasma half‐life and a 1000‐fold increase in AUC 0–24 h when compared with Hycamtin administered at equivalent doses (5 mg/kg). In contrast to the great extension in exposure time, SM /Chol liposomal topotecan increased the life span of mice with established LAN ‐1 neuroblastoma tumors only modestly in a subcutaneous and systemic model. The extension in exposure time may still not be sufficient and the formulation may require further optimization. In the future, liposomal topotecan will be assessed in combination with high‐dose radiotherapy such as 131 I‐metaiodobenzylguanidine, and immunotherapy treatment modalities currently used in neuroblastoma therapy.
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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".