Liposomal formulations of carboplatin injected by convection-enhanced delivery increases the median survival time of F98 glioma bearing rats
Bibliographic record
Abstract
BACKGROUND: Effectiveness of chemotherapy for treating glioblastoma (GBM) brain tumors is hampered by the blood-brain barrier which limits the entry into the brain of most drugs from the blood. To bypass this barrier, convection-enhanced delivery (CED) was proposed to directly inject drugs in tumor. However, the benefit of CED may be hampered when drugs diffuse outside the tumor to then induce neurotoxicity. Encapsulation of drugs into liposome aims at increasing tumor cells specificity and reduces neurotoxicity. However, the most appropriate liposomal formulation to inject drugs into brain tumor by CED still remains to be determined. In this study, four liposomal carboplatin formulations were prepared and tested in vitro on F98 glioma cells and in Fischer rats carrying F98 tumor implanted in the brain. Impact of pegylation on liposomal surface and relevance of positive or negative charge were assessed. RESULTS: The cationic non-pegylated (L1) and pegylated (L2) liposomes greatly improved the toxicity of carboplatin in vitro compared to free carboplatin, whereas only a modest improvement and even a reduction of efficiency were measured with the anionic non-pegylated (L3) and the pegylated (L4) liposomes. Conversely, only the L4 liposome significantly increased the median survival time of Fisher rats implanted with the F98 tumor, compared to free carboplatin. Neurotoxicity assays performed with the empty L4' liposome showed that the lipid components of L4 were not toxic. These results suggest that the positive charge on liposomes L1 and L2, which is known to promote binding to cell membrane, facilitates carboplatin accumulation in cancer cells explaining their higher efficacy in vitro. Conversely, negatively charged and pegylated liposome (L4) seems to diffuse over a larger distance in the tumor, and consequently significantly increased the median survival time of the animals. CONCLUSIONS: Selection of the best liposomal formulation based on in vitro studies or animal model can result in contradictory conclusions. The negatively charged and pegylated liposome (L4) which was the less efficient formulation in vitro showed the best therapeutic effect in animal model of GBM. These results support that relevant animal model of GBM must be considered to determine the optimal physicochemical properties of liposomal formulations.
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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) | 0.001 | 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 | 0.001 |
| 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".