Repurposing chlorpromazine for anti-leukaemic therapy with the drug-in-cyclodextrin-in-liposome nanocarrier platform
Bibliographic record
Abstract
Acute myeloid leukaemia (AML) accounts for 30 % of adult leukaemia cases, predominantly affecting individuals over 60. The standard "7 + 3" intensive chemotherapy regimen is unsuitable for many elderly patients, contributing to AML's poor prognosis. While progress in drug therapies has been made, breakthroughs remain limited, indication-specific, and slow to expand. Drug repurposing offers a faster route to therapy development, while nanocarrier encapsulation broadens the scope of viable drug candidates. Chlorpromazine (CPZ) is an antipsychotic which has been identified as a potential anti-leukaemic agent. Due to its ability to cross the blood-brain barrier, it is likely to cause central nervous system (CNS) effects. The drug-in-cyclodextrin-in-liposome (DCL) nanocarrier platform enables the formulation of CPZ encapsulated with cyclodextrins (CDs) such as HP-γ-CD, SBE-β-CD, and Sugammadex. The CD/CPZ formulations were equally, or more efficient than free CPZ in inducing AML cell death. Uptake of the DCL in AML cells quickly reached saturation, with minimal differences among formulations, except for SBE-β-CD. When injected intravenously in zebrafish larvae, the different DCLs did not differ in biodistribution, and no brain accumulation was observed at two days post-injection. These DCL-based CPZ formulations maintain anti-leukaemic activity, avoid CNS accumulation, and allow drug availability adjustments based on the included CD.
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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 | 0.001 |
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".