Surfactant‐Stripped Cabazitaxel Micelles Stabilized by Clotrimazole or Mifepristone
Bibliographic record
Abstract
Abstract Taxane chemotherapy formulations are used to treat advanced cancers, but limited solubility and propensity for aggregation in water complicates their development. Many involve drug dissolution in organic solvents and liquid surfactants, or use of lyophilization and reconstitution approaches. “Surfactant‐stripping,” has been previously reported, in which hydrophobic drugs were first dispersed in Pluronic (Poloxamer) surfactant, then subjected to membrane processing below the critical micelle temperature, to remove free and loose surfactant while retaining the active cargo. In the present work, stabilized, surfactant‐stripped (sss) cabazitaxel (CTX) micelles with potential for long‐term aqueous storage are developed. Some 50 hydrophobic co‐loaders cargos are screened for capacity to prevent aggregation of CTX, of which approximately 10 are effective. Further screening identifies the antifungal clotrimazole and the abortificant mifepristone as the most effective stabilizers for sss‐CTX micelles, via interference with the CTX aggregation process. Micelles remain stable for hundreds of days in aqueous storage and suppress the growth of orthotopic 4T1 murine mammary tumors. Pharmacokinetics, tubulin stabilization, and neutropenia induction of sss‐CTX are generally comparable to a TWEEN‐80 CTX formulation. These data reveal sss‐CTX as a taxane delivery vehicle with a high drug‐to‐surfactant ratio and capacity for extended aqueous storage.
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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".