Targeted delivery and enhanced gene-silencing activity of centrally modified folic acid–siRNA conjugates
Bibliographic record
Abstract
One of the major hurdles in RNAi research has been the development of safe and effective delivery systems for siRNAs. Although various chemical modifications have been proposed to improve their pharmacokinetic behaviour, their delivery to target cells and tissues presents many challenges. In this work, we implemented a receptor-targeting strategy to selectively deliver siRNAs to cancer cells using folic acid as a ligand. Folic acid is capable of binding to cell-surface folate receptors with high affinity. These receptors have become important molecular targets for cancer research as they are overexpressed in numerous cancers despite being expressed at low levels in normal tissues. Employing a post-column copper-catalyzed alkyne-azide cycloaddition (CuAAC), we report the synthesis of siRNAs bearing folic acid modifications at different positions within the sense strand. In the absence of a transfection carrier, these siRNAs were selectively taken up by cancer cells expressing folate receptors. We show that centrally modified folic acid-siRNAs display enhanced gene-silencing activity against an exogenous gene target (∼80% knockdown after 0.75 μM treatment) and low cytotoxicity. In addition, these siRNAs achieved potent dose-dependent knockdown of endogenous Bcl-2, an important anti-apoptotic gene.
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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) | 0.001 | 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 | 0.001 |
| 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".