Amino Acid-Substituted Gemini Surfactant-Based Nanoparticles as Safe and Versatile Gene Delivery Agents
Bibliographic record
Abstract
Gene based therapy represents an important advance in the treatment of diseases that heretofore have had either no treatment or cure. To capitalize on the true potential of gene therapy, there is a need to develop better delivery systems that can protect these therapeutic biomolecules and deliver them safely to the target sites. Recently, we have designed and developed a series of novel amino acid-substituted gemini surfactants with the general chemical formula C(12)H(25) (CH(3))(2)N(+)-(CH(2))(3)-N(AA)-(CH(2))(3)-N(+) (CH(3))(2)-C(12)H(25) (AA= glycine, lysine, glycyl-lysine and, lysyl-lysine). These compounds were synthesized and tested in rabbit epithelial cells using a model plasmid and a helper lipid. Plasmid/gemini/lipid (P/G/L) nanoparticles formulated using these novel compounds achieved higher gene expression than the nanoparticles containing the parent unsubstituted compound. In this study, we evaluated the cytotoxicity of P/G/L nanoparticles and explored the relationship between transfection efficiency/toxicity and their physicochemical characteristics (such as size, binding properties, etc.). An overall low toxicity is observed for all complexes with no significant difference among substituted and unsubstituted compounds. An interesting result revealed by the dye exclusion assay suggests a more balanced protection of the DNA by the glycine and glycyl-lysine substituted compounds. Thus, the higher transfection efficiency is attributed to the greater biocompatibility and flexibility of the amino acid/peptide-substituted gemini surfactants and demonstrates the feasibility of using amino acid-substituted gemini surfactants as gene carriers for the treatment of diseases affecting epithelial tissue.
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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".