Preparation, Characterization, Pharmacokinetic, and Therapeutic Potential of Novel 6-Mercaptopurine-Loaded Oral Nanomedicines for Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy in children. It requires a long and rigorous course of chemotherapy treatments. 6-Mercaptopurine (6-MP) is one of the primary drugs used in chemotherapy. Unfortunately, its efficacy has been limited due to its insolubility, poor bioavailability and serious adverse effects. To overcome these drawbacks, we constructed 6-mercaptopurine (6-MP)-loaded nanomedicines (6-MPNs) with biodegradable poly(lactide-co-glycolide) (PLGA) to enhance the anticancer efficacy of 6-MP. METHODS: We prepared the 6-MPNs using a double-emulsion solvent evaporation method, characterizing them for the physicochemical properties. We then investigated the plasma, intestinal region and other organs in Sprague Dawley (SD) rats for pharmacokinetics. Additionally, we evaluated its anticancer efficacy in vitro on the human T leukemia cell line Jurkat and in vivo on the ALL model mice. RESULTS: The 6-MPNs were spherical in shape with uniform particle size and high encapsulation efficiency. The in vitro release profile showed that 6-MPNs exhibited a burst release that a sustained release phase then followed. The apoptosis assay demonstrated that 6-MPNs could improve the in vitro cytotoxicity in Jurkat cells. Pharmacokinetics profiles revealed that 6-MPNs had improved oral bioavailability. Tissue distribution experiments indicated that 6-MPNs increased the duodenum absorption of 6-MP, at the same time having a low accumulation of the toxic metabolites of 6-MP. The in vivo pharmacodynamics study revealed that 6-MPNs could prolong the survival time of the ALL model mice. The prepared 6-MPNs, therefore, have superior properties in terms of anticancer efficacy against ALL with reduced systemic toxicity. CONCLUSION: Our nanomedicines provide a promising delivery strategy for 6-MP; they offer a simple preparation method and high significance for clinical translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".