Design and Evaluation of Gemini Surfactant-Based Lipoplexes Modified with Cell-Binding Peptide for Targeted Gene Therapy in Melanoma Model
Bibliographic record
Abstract
Purpose Achieving successful gene therapy requires delivery of a gene vector specifically to the targeted tissue with efficient expression and a good safety profile. The objective of this work was to develop, characterize and determine if a novel gemini surfactant-based lipoplex systems, modified with a cancer-targeting peptide p18-4, could serve this role. Methods The targeting peptide p18-4 was either chemically coupled to a gemini surfactant backbone or physically co-formulated with the lipoplexes. The influence of targeting ligand and formulation strategies on essential physicochemical properties of the lipoplexes was evaluated by dynamic light scattering and small angle X-ray scattering techniques. In vitro transfection activity and cellular toxicity of lipoplexes were assessed in a model human melanoma cell line. Results All lipoplexes zeta potential and particle size were optimal for cellular uptake and physical stability of the system. The lipoplexes adopted an inverted-hexagonal lipid arrangement. The lipoplexes modified with the peptide showed no significant changes in physicochemical properties or lipoplex assembly. The modification of the lipoplexes with the targeting peptide significantly enhanced protein expression 2-6 fold compared to non-modified lipoplexes. In addition, p18-4 modified lipoplexes significantly improved the safety of the lipoplexes. The ability of the p18-4 modified lipoplexes to selectively express the model protein was confirmed by using healthy human epidermal keratinocytes (HEKa). Conclusion The gemini surfactant-based lipoplexes modified with p18-4 peptide showed significantly higher efficiency and safety compared to the system that did not contain a cancer targeting peptide and provided evidence for their potential application to achieve targeted melanoma gene therapy.
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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) | 0.000 | 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".