Suppressing t(4;11) Acute Leukemia by Lipopolymer Nanoparticle Delivery of siRNA Targeting <i>KMT2A::AFF1</i> with Enhanced Extrahepatic Delivery
Bibliographic record
Abstract
Effective siRNA delivery in acute lymphoblastic leukemia (ALL) is limited by preferential hepatic accumulation. To address this, a lipopolymer (PEI-C) is developed by conjugating lipid to polyethylenimine and formulated lipopolymer nanoparticles (LPNPs) via complexation with siRNA. The siRNA delivery efficiency of LPNPs is evaluated in vitro in t(4;11)-positive ALL cells (RS4;11 and SEM) as well as "normal" peripheral blood mononuclear cells (PBMCs) from human donors and bone marrow stromal cells (BMSCs) from mice. Molecular effects are assessed by quantifying target mRNA silencing and downstream apoptosis. In vivo biodistribution and therapeutic efficacy are examined in mouse models. LPNPs demonstrated significantly higher siRNA uptake than commercial reagents in PBMCs and BMSCs, reaching siRNA uptakes of 87.2% and 93.0% in RS4;11 and SEM cells, respectively. Molecular analyses revealed effective silencing of KMT2A::AFF1 mRNA (≈80% in RS4;11), accompanied by BCL2 downregulation and increased apoptosis. In vivo, LPNPs showed efficient siRNA biodistribution to leukemia-associated organs (spleen and bone marrow) and significantly reduced leukemia burden in a systemic RS4;11 xenograft mouse model and improved survival. These findings suggest that PEI-C-formulated LPNPs present a promising avenue for therapeutic siRNA delivery in ALL, effectively targeting leukemia-associated organs, and warrant further exploration in clinical studies.
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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 | 0.000 |
| 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) | 0.000 | 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".