Altered Organ Accumulation of Oligonucleotides Using Polyethyleneimine Grafted With Poly(ethylene Oxide) or Pluronic as Carriers
Bibliographic record
Abstract
Passive targeting provides a simple strategy based on natural properties of the carriers to deliver DNA molecules to desired compartments. Polyethylenimine (PEI) is a potent non-viral system that has been known to deliver efficiently both plasmids and oligonucleotides (ODNs) in vitro. However, in vivo systemic administration of DNA/PEI complexes has encountered significant difficulties because these complexes are toxic and have low biodistribution in target tissues. This study evaluates PEI grafted with poly(ethylene oxide) (PEO(8K)-g-PEI(2K)) and PEI grafted with non-ionic amphiphilic block copolymer, Pluronic P85 (P85-g-PEI(2K)) as carriers for systemic delivery of ODNs. Following i.v. injection an antisense ODN formulated with PEO(8K)-g-PEI(2K) accumulated mainly in kidneys, while the same ODN formulated with P85-g-PEI(2K) was found almost exclusively in the liver. Furthermore, in the case of the animals injected with the P85-g-PEI(2K)-based complexes most of the ODN was found in hepatocytes, while only a minor portion of ODN was found in the lymphocyte/monocyte populations. The results of this study suggest that formulating ODN with PEO(8K)-g-PEI(2K) and P85-g-PEI(2K) carriers allows targeting of the ODN to the liver or kidneys, respectively. The variation in the tissue distribution of ODN observed with the two carriers is probably due to the different hydrophilic-lipophilic balance of the polyether chains grafted to PEI in these molecules. Therefore, polyether-grafted PEI carriers provide a simple way to enhance ODN accumulation in a desired compartment without the need of a specific targeting moiety.
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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".