To Conjugate or to Package? A Look at Targeted siRNA Delivery Through Folate Receptors
Bibliographic record
Abstract
RNA interference (RNAi) applications have evolved from experimental tools to study gene function to the development of a novel class of gene-silencing therapeutics. Despite decades of research, it was not until August 2018 that the US FDA approved the first-ever RNAi drug, marking a new era for RNAi therapeutics. Although there are many limitations associated with the inherent structure of RNA, delivery to target cells and tissues remains the most challenging. RNAs are unable to diffuse across cellular membranes due to their large size and polyanionic backbone and, therefore, require a delivery vector. RNAi molecules can be conjugated to a targeting ligand or packaged into a delivery vehicle. Alnylam has used both strategies in their FDA-approved formulations to achieve efficient delivery to the liver. To harness the full potential of RNAi therapeutics, however, we must be able to target additional cells and tissues. One promising target is the folate receptor α, which is overexpressed in a variety of tumors despite having limited expression and distribution in normal tissues. Folate can be conjugated directly to the RNAi molecule or used to functionalize delivery vehicles. In this review, we compare both delivery strategies and discuss the current state of research in the area of folate-mediated delivery of RNAi molecules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".