Lipopolymers as the Basis of Non-Viral Delivery of Therapeutic siRNA Nanoparticles in a Leukemia (MOLM-13) Model
Bibliographic record
Abstract
Small interfering RNA (siRNA) therapy in acute myeloid leukemia (AML) is a promising strategy as the siRNA molecule can specifically target proteins involved in abnormal cell proliferation. The development of a clinically applicable method for delivering siRNA molecules is imperative due to the challenges involved in effectively delivering the siRNA into cells. We investigated the delivery of siRNA to AML MOLM-13 cells with the use of two lipid-substituted polyethyleneimines (PEIs), a commercially available reagent (Prime-Fect) and a recently reported reagent with improved lipid substitution (PEI1.2k-PHPA-Lin9). The siRNAs utilized in this study were targeting the oncogenes FLT3 and KMT2A::MLLT3. Both lipopolymers gave similar-size siRNA complexes (210–220 nm) with positive ζ-potentials (+17 to +25 mV). While the binding efficiency of both lipopolymers to siRNA were similar, PEI1.2k-PHPA-Lin9 complexes were more resistant to heparin-induced dissociation. The quantitative analysis of gene silencing performed by qPCR as well as immunostaining/flow cytometry indicated significant reduction in both FLT3 expression and FLT3 protein after specific siRNA delivery. The desired inhibition of cell growth was attained with both FLT3 and KMT2A::MLLT3 siRNAs, and the combination provided more potent effects in both cell growth and colony formation assays. Induction of apoptosis was confirmed after specific siRNA treatments using the Annexin V assay. Using Luc(+) MOLM-13 cells, the growth of the xenografted cells was shown to be retarded with Prime-Fect-delivered FLT3 siRNA, unlike the siRNA delivered with PEI1.2k-PHPA-Lin9. These results demonstrate the potential of designed lipopolymers in implementing RNAi (via delivery of siRNA) for inhibition of leukemia growth and provide evidence for the feasibility of targeting different oncogenes using siRNA-mediated therapy.
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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".