Optimizing Liposomal Cisplatin Efficacy through Membrane Composition Manipulations
Bibliographic record
Abstract
The first liposomal formulation of cisplatin to be evaluated clinically was SPI-077. Although the formulation demonstrated enhanced cisplatin tumor accumulation in preclinical models it did not enhance clinical efficacy, possibly due to limited cisplatin release from the formulation localized within the tumor. We have examined a series of liposomal formulations to address the in vivo relationship between cisplatin release rate and formulation efficacy in the P388 murine leukemia model. The base formulation of phosphatidylcholine: phosphatidylglycerol: cholesterol was altered in the C18 and C16 phospholipid content to influence membrane fluidity and thereby impacting drug circulation lifetime and drug retention. Phase transition temperatures (T(m)) ranged from 42-55°C. The high T(m) formulations demonstrated enhanced drug retention properties accompanied by low antitumor activity while the lowest T(m) formulations released the drug too rapidly in the plasma, limiting drug delivery to the tumor which also resulted in low antitumor activity. A formulation composed of DSPC : DPPC : DSPG : Chol; (35 : 35 : 20 : 10) with an intermediate drug release rate and a cisplatin plasma half-life of 8.3 hours showed the greatest antitumor activity. This manuscript highlights the critical role that drug release rates play in the design of an optimized drug delivery vehicle.
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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 | 0.001 |
| 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 | 0.000 | 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".