Effective Non-Viral Delivery of siRNA to Acute Myeloid Leukemia Cells with Lipid-Substituted Polyethylenimines
Bibliographic record
Abstract
Use of small interfering RNA (siRNA) is a promising approach for AML treatment as the siRNA molecule can be designed to specifically target proteins that contribute to aberrant cell proliferation in this disease. However, a clinical-relevant means of delivering siRNA molecules must be developed, as the cellular delivery of siRNA is problematic. Here, we report amphiphilic carriers combining a cationic polymer (2 kDa polyethyleneimine, PEI2) with lipophilic moieties to facilitate intracellular delivery of siRNA to AML cell lines. Complete binding of siRNA by the designed carriers was achieved at a polymer:siRNA ratio of ≈ 0.5 and led to siRNA/polymer complexes of ≈ 100 nm size. While the native PEI2 did not display cytotoxicity on AML cell lines THP-1, KG-1 and HL-60, lipid-modification on PEI2 slightly increased the cytotoxicity, which was consistent with increased interaction of polymers with cell membranes. Cellular delivery of siRNA was dependent on the nature of lipid substituent and the extent of lipid substitution, and varied among the three AML cell lines used. Linoleic acid-substituted polymers performed best among the prepared polymers and gave a siRNA delivery equivalent to better performing commercial reagents. Using THP-1 cells and a reporter (GFP) and an endogenous (CXCR4) target, effective silencing of the chosen targets was achieved with 25 to 50 nM of siRNA concentrations, and without adversely affecting subsequent cell growth. We conclude that lipid-substituted PEI2 can serve as an effective delivery of siRNA to leukemic cells and could be employed in molecular therapy of leukemia.
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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 | 0.000 |
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 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".