Biodistribution of Therapeutic Small Interfering RNAs Delivered with Lipid-Substituted Polyethylenimine-Based Delivery Systems
Bibliographic record
Abstract
Small interfering RNAs (siRNAs) have emerged as a powerful tool to manipulate gene expression in vitro . However, their potential therapeutic application encounters significant challenges, such as degradation in vivo, limited cellular uptake, and restricted biodistribution, among others. This study evaluates the siRNA delivery efficiency of three different lipid-substituted polyethylenimine (PEI)-based carriers, named Leu-Fect A–C, to different organs in vivo, including xenograft tumors, when injected into the bloodstream of mice. The siRNA analysis was undertaken by stem-loop RT-PCR, followed by qPCR or digital droplet PCR. Formulating siRNAs with a Leu-Fect series of carriers generated nanoparticles that effectively delivered the siRNAs into K652 and MV4–11 cells, both models of leukemia. The Leu-Fect carriers were able to successfully deliver BCR-Abl and FLT3 siRNAs into leukemia xenograft tumors in mice. All three carriers demonstrated significantly enhanced siRNA delivery into organs other than the liver, including the xenograft tumors. Preferential biodistribution of siRNAs was observed in the lungs and spleen. Among the delivery systems, Leu-Fect A exhibited the highest biodistribution into organs. In conclusion, lipid-substituted PEI-based delivery systems offer improvements in addressing pharmacokinetic challenges associated with siRNA-based therapies, thus opening avenues for their potential translation into clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".