Lipothioureas as Lipids for Gene Transfection: A Review
Bibliographic record
Abstract
Non-viral gene therapy requires innovative strategies to achieve higher transfection efficacy. A few years ago, our group proposed bioinspired lipids whoseinteraction with DNA was not based on ionic interactions, but on hydrogen bonds. We thusdeveloped lipids bearing a thiourea head which allowed an interaction with DNAphosphates through hydrogen bonds. After a proof of concept with a lipid bearing threethiourea functions, a molecular and cellular screening was performed by varying all partsof the lipids: the hydrophobic anchor, the spacer, the linker, and the thiourea head. Twolipothiourea-based structures were identified as highly efficient in vitro transfecting agents.The lipothioureas were shown to reduce non specific interactions with cell membranes anddeliver their DNA content intracellularly more efficiently, as compared to cationiclipoplexes. These lipids could deliver siRNA efficiently and allowed specific cell targetingin vitro. In vivo, thiourea lipoplexes presented a longer retention time in the blood and lessaccumulation in the lungs after an intravenous injection in mice. They also inducedluciferase gene expression in muscle and tumor after local administration in mice.Therefore, these novel lipoplexes represent an excellent alternative to cationic lipoplexes astransfecting agents. In this review we will focus on the structure activity studies thatpermitted the identification of the two most efficient thiourea lipids.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".