Cationic lipopolymer based siRNA delivery for experimental lung cancer treatment
Bibliographic record
Abstract
Conventional therapeutic approaches often struggle to address "undruggable" or intracellular targets, limiting their effectiveness in treating critical diseases. RNA interference (RNAi), particularly through the delivery of short interfering RNAs (siRNAs), has emerged as a promising alternative. In this study, we evaluated the potential of a series of cationic lipopolymers, including ALL-Fect, Leu-Fect, and Prime-Fect, for delivering siRNAs targeting CDC20, Survivin, and STAT5 in lung cancer cell models. These polymers exhibited strong siRNA binding (BC50: 0.17 ± 0.04 to 1.67 ± 0.31) and dissociation (DC50: 57.9 to 13.6 U/mL) properties, forming nanoparticles with ζ-potential of -15 to +23 mV, and particles sizes of 150 to 400 nm suitable for efficient cellular uptake, achieving over 75 % FAM-positive cell populations in lung cancer cells. Remarkably, these complexes demonstrated significant cell killing effects with specific siRNAs even at a low siRNA concentration (20 nM), with maximal effects observed at a polymer/siRNA ratio of 5:1 ratio and 40 nM siRNA concentration, resulting in over 75 % cell killing. The performance of lipid nanoparticles (LNPs) for the delivery of the specific siRNAs was minimal compared to the lipopolymeric carriers under similar conditions. These findings underscore the potential of lipopolymers as safe and effective non-viral vectors for siRNA-based lung cancer therapeutics.
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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 | 0.001 |
| 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".