Impact of the Core Chemistry of Self‐Assembled Spherical Nucleic Acids on their <i>In Vitro</i> Fate
Bibliographic record
Abstract
Nucleic acid therapeutics (NATs), such as mRNA, small interfering RNA or antisense oligonucleotides are extremely efficient tools to modulate gene expression and tackle otherwise undruggable diseases. Spherical nucleic acids (SNAs) can efficiently deliver small NATs to cells while protecting their payload from nucleases, and have improved biodistribution and muted immune activation. Self-assembled SNAs have emerged as nanostructures made from a single DNA-polymer conjugate with similar favorable properties as well as small molecule encapsulation. However, because they maintain their structure by non-covalent interactions, they might suffer from disassembly in biologically relevant conditions, especially with regard to their interaction with serum proteins. Here, we report a systematic study of the factors that govern the fate of self-assembled SNAs. Varying the core chemistry and using stimuli-responsive disulfide crosslinking, we show that extracellular stability upon binding with serum proteins is important for recognition by membrane receptors, triggering cellular uptake. At the same time, intracellular dissociation is required for efficient therapeutic release. Disulfide-crosslinked SNAs combine these two properties and result in efficient and non-toxic unaided gene silencing therapeutics. We anticipate these investigations will help the translation of promising self-assembled structures towards in vivo gene silencing applications.
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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 | 0.001 |
| 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".