Development of modular polymeric nanoparticles for drug delivery using amine reactive chemistry
Bibliographic record
Abstract
Curcumin has been explored for its anti-cancer potential, but is severely limited by its hydrophobicity and sensitivity to light and water. In this study, poly (lactic-co-glycolic) acid (PLGA) nanoparticles (NPs) were synthesized to encapsulate curcumin via single emulsion method to improve curcumin stability and bioavailability. The PLGA NPs were coated with oligomeric chitosan (COS) and RGD peptide (a peptide consisting of Arg-Gly-Asp) using amine-reactive chemistry (NHS and EDC). Both COS and RGD had been previously shown to accumulate and target many different types of cancer cells. NPs were characterised based on size distribution, zeta potential, and binding efficiency of RGD peptide. They were also evaluated on encapsulation efficiency, and stability, of curcumin within the NPs. OVCAR-3 cancer cells were treated with COS and RGD-coated PLGA NPs loaded with Coumarin-6 dye for fluorescent imaging of cell uptake. They were also treated with curcumin-loaded NPs to determine cytotoxicity and effectiveness of delivery. The NPs exhibited size distribution and zeta potential within expected values, though binding efficiency of RGD was low. Curcumin-loaded NPs showed significant increase in cytotoxicity over free (unencapsulated) curcumin, and void (empty) NPs, suggesting successful delivery of curcumin as an anti-cancer agent; the performance of COS and RGD coated NPs over bare PLGA NPs was inconclusive, however, optimization will be required to improve formulation during the coating steps. This method of NP synthesis serves as proof of concept for a modular solution to the development of various coated polymeric NPs for other drugs or applications.
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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) | 0.000 | 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".