<i>In Vitro</i> Characterization of Two Novel Biodegradable Vectors for the Delivery of Radiolabeled Antisense Oligonucleotides
Bibliographic record
Abstract
The development of antisense oligonucleotides suitable for tumor targeting applications is hindered by low stability and bioavailability of oligonucleotides in vivo and by the absence of efficient and safe vectors for oligonucleotide delivery. Stabilization in vivo has been achieved through chemical modification of oligonucleotides by various means, but effective approaches to enhance their intracellular delivery are lacking. This study reports on the characterization in vitro of a fully phosphorothioated 20-mer oligonucleotide, complementary to p21 mRNA, radiolabeled with fluorine-18 using a thiol reactive prosthetic group. The potential of two novel synthetic block copolymers containing grafted polyamines on their hydrophobic blocks for vector-assisted cell delivery was studied in vitro. Extensive cellular uptake studies were performed in human colon carcinoma cell lines with enhanced or deficient p21 expression to evaluate and compare the uptake mechanism of naked and vectorized radiolabeled formulations. Uptake studies with the two novel biodegradable vectors showed a moderate increase in cell uptake of the radiofluorinated antisense oligonucleotide. The two vectors show, however, promising advantages over conventional lipidic vectors regarding their biocompatibility and subcellular distribution.
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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".