Tuning the Biological Properties of Spherical Nucleic Acids with Phosphate Backbone Modified Oligonucleotides
Bibliographic record
Abstract
The increasing number of nucleic acid-based therapeutics demonstrates the potential to treat diseases at the genetic level. Although oligonucleotides show clinical potential, challenges remain including nuclease degradation, rapid clearance when administered systemically, low cell permeability, and limited distribution to tissues of interest. This is largely imparted by the polyanionic phosphate backbone, which produces unfavourable electrostatic interactions at cell membranes. As a result, their clinical translation is dependent on delivery technologies that improve stability, facilitate cell entry, and increase target affinity. Spherical nucleic acids (SNAs) consist of radially orienting linear nucleic acids onto a nanoparticle core, conferring them a three-dimensional, spherical architecture. These structures enter cells readily and display distinct properties that are independent of their nanoparticle core. Accordingly, we decided to replace the intrinsically anionic phosphodiester linkage of DNA with a phosphoramidate linkage (P-N), allowing us to incorporate new functionality at the phosphate backbone. With this handle, we inserted cationic and hydrophobically modified functional groups that were compatible with nanoscale architectures, giving rise to new properties relevant in biological contexts. Specifically, amine and guanidinium derivatized functional groups provided SNAs with a ~10-fold increase in cell uptake at early incubation times compared with unmodified SNAs. This demonstrates that we can tune the behaviour of SNAs with phosphate backbone modifications in a highly controlled manner. We hypothesize that the stringent control over location and placement of functional groups within the SNA framework will afford them favourable interactions at cell membranes, not only increasing their cell uptake, but also access to alternative uptake mechanisms and potency as therapeutics.
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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".