Dual delivery of nucleic acids and PEGylated-bisphosphonates <i>via</i> calcium phosphate nanoparticles
Bibliographic record
Abstract
Abstract Despite many years of research and a few success stories with gene therapeutics, efficient and safe DNA delivery remains a major bottleneck for the clinical translation of gene-based therapies. Gene transfection with calcium phosphate (CaP) nanoparticles brings the advantages of low toxicity, high DNA entrapment efficiency and good endosomal escape properties. The macroscale aggregation of CaP nanoparticles can be easily prevented through surface coating with bisphosphonate conjugates. Bisphosphonates, such as alendronate, recently showed promising anticancer effects. However, their poor cellular permeability and preferential bone accumulation hamper their full application in chemotherapy. Here, we investigated the dual delivery of plasmid DNA and alendronate using CaP nanoparticles, with the goal to facilitate cellular internalization of both compounds and potentially achieve a combined pharmacological effect on the same or different cell lines. A pH-sensitive poly(ethylene glycol)-alendronate conjugate was synthetized and used to formulate stable plasmid DNA-loaded CaP nanoparticles. These particles displayed good transfection efficiency in cancer cells and a strong cytotoxic effect on macrophages. The in vivo transfection efficiency, however, remained low, calling for an improvement of the system, possibly with respect to the extent of particle uptake and their physical stability. Graphical abstract
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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) | 0.003 | 0.001 |
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".