Targeting of Lipid-Protamine-DNA (LPD) Lipopolyplexes Using RGD Motifs
Bibliographic record
Abstract
The incorporation of pegylated lipid into Lipid-Protamine-DNA (LPD-PEG) lipopolyplexes causes a decrease of their in vitro transfection activity. This can be partially attributed to a reduction in particle binding to cells. To restore particle binding and specifically target LPD formulations to tumor cells, the lipid-peptide conjugate DSPE-PEG5K-succinyl-ACDCRGDCFCG-COOH (DSPE-PEG5K-RGD-4C) was generated and incorporated into LPD formulations (LPD-PEG-RGD). LPD-PEG-RGD was characterized with respect to its biophysical and biological properties. The Incorporation of DSPE-PEG5K-RGD-4C ligands into LPD formulations results in a 5 and a 15 fold increase in the LPD-PEG-RGD binding and uptake, respectively, over an LPD-PEG formulation. Enhancement of binding and uptake resulted in a 100 fold enhancement of transfection activity. Moreover, this transfection enhancement was specific to cells expressing appropriate integrin receptors (MDA-MB-231). Huh7 cells, known for their low level of alphavbeta3 and alphavbeta5 integrin expression, failed to show RGD mediated transfection enhancement. This transfection enhancement can be abolished in a competitive manner using free RGD peptide, but not an RGE control peptide. Results demonstrated RGD mediated enhanced LPD-PEG cell binding and transfection in cells expressing the integrin receptor. These formulations provide the basis for effective, targeted, systemic gene delivery.
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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".