Lipopolymer/siRNA Nanoparticles Targeting the Signal Transducer and Activator of Transcription 5A Disrupts Proliferation of Acute Lymphoblastic Leukemia
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Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The therapeutic potential of small interfering RNAs (siRNAs) in gene-targeted treatments is substantial, but their suboptimal delivery impedes widespread clinical applications. Critical among these is the inability of siRNAs to traverse the cell membranes due to their anionic nature and high molecular weight. This limitation is particularly pronounced in lymphocytes, which pose additional barriers due to their smaller size and scant cytoplasm. Addressing this, we introduce an innovative lipid-conjugated polyethylenimine lipopolymer platform, engineered for delivery of therapeutic siRNAs into lymphocytes. This system utilizes the cationic nature of the polyethylenimine for forming stable complexes with anionic siRNAs, while the lipid component facilitates cellular entry of siRNA. The resulting lipopolymer/siRNA complexes are termed lipopolymer nanoparticles (LPNPs). We comprehensively profiled the efficacy of this platform in human peripheral blood mononuclear cells (PBMCs) as well as in vitro and in vivo models of acute lymphoblastic leukemia (ALL), emphasizing the inhibition of the oncogenic signal transducer and activator of transcription 5A ( STAT5A) gene. The lipopolymers demonstrated high efficiency in delivering siRNA to ALL cell lines (RS4;11 and SUP-B15) and primary patient cells, effectively silencing the STAT5A gene. The resultant gene silencing induced apoptosis and significantly reduced colony formation in vitro . Furthermore, in vivo studies showed a significant decrease in tumor volumes without causing substantial toxicity. The lipopolymers did not induce the secretion of proinflammatory cytokines (IL-6, TNF-α, and INF-γ) in PBMCs from healthy volunteers, underscoring their immune safety profile. Our observations indicate that LPNP-based siRNA delivery systems offer a promising therapeutic approach for ALL in terms of both safety and therapeutic efficacy.
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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 | 0.001 |
| 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 it