Investigation of water‐insoluble hydrophobic polyethylenimines as <scp>RNAi</scp> vehicles in chronic myeloid leukemia therapy
Bibliographic record
Abstract
The discovery of RNA interference (RNAi) more than two decades ago opened avenues for avant-garde cancer treatments that possess the ability to evade issues hampering current chemotherapeutic strategies, owing to its specific gene sequence-driven mechanism of action. A potent short interfering RNA (siRNA) delivery vehicle designed to overcome physiological barriers is imperative for successful RNAi therapy. For this purpose, this study explored the characteristics and therapeutic efficacy of low-molecular weight (MW) polyethylenimine (PEI) with high cholesterol substitution, yielding water-insoluble polymers, in chronic myeloid leukemia (CML) K562 cells. A strong impact of cholesterol grafting on the physicochemical attributes of the resultant polymers and their corresponding complexes with siRNA was observed, with the siRNA binding capacity of polymers increasing and complex dissociation sensitivity decreasing with increase in cholesterol content of the polymers. The modified polymer complexes were significantly smaller in size and possessed higher cationic charge compared to the parent polymer. The interaction with anionic heparan sulfate preoteoglycans present on the cell surface was significant in cellular uptake of the complexes. The therapeutic efficacy of siRNA/polymer complexes was reflected in their ability to effectively silence the reporter green fluorescent protein gene and endogenous CML oncogene BCR-ABL as well as significantly inhibit colony formation by K562 cells post BCR-ABL silencing. The results of this study demonstrated beneficial effects of high levels of hydrophobic substitution on low MW PEI on their functional performance bestowing them the potential to be potent RNAi agents for CML therapy.
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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) | 0.000 | 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 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".