Layer-by-Layer Assembled Antisense DNA Microsponge Particles for Efficient Delivery of Cancer Therapeutics
Bibliographic record
Abstract
Antisense oligonucleotides can be employed as a potential approach to effectively treat cancer. However, the inherent instability and inefficient systemic delivery methods for antisense therapeutics remain major challenges to their clinical application. Here, we present a polymerized oligonucleotides (ODNs) that self-assemble during their formation through an enzymatic elongation method (rolling circle replication) to generate a composite nucleic acid/magnesium pyrophosphate sponge-like microstructure, or DNA microsponge, yielding high molecular weight nucleic acid product. In addition, this densely packed ODN microsponge structure can be further condensed to generate polyelectrolyte complexes with a favorable size for cellular uptake by displacing magnesium pyrophosphate crystals from the microsponge structure. Additional layers are applied to generate a blood-stable and multifunctional nanoparticle via the layer-by-layer (LbL) assembly technique. By taking advantage of DNA nanotechnology and LbL assembly, functionalized DNA nanostructures were utilized to provide extremely high numbers of repeated ODN copies for efficient antisense therapy. Moreover, we show that this formulation significantly improves nucleic acid drug/carrier stability during in vivo biodistribution. These polymeric ODN systems can be designed to serve as a potent means of delivering stable and large quantities of ODN therapeutics systemically for cancer treatment to tumor cells at significantly lower toxicity than traditional synthetic vectors, thus enabling a therapeutic window suitable for clinical translation.
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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".