Optimization of Curcumin-Loaded PEG-PLGA Nanoparticles by GSH Functionalization: Investigation of the Internalization Pathway in Neuronal Cells
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Bibliographic record
Abstract
One major challenge in the field of nanotherapeutics is to increase the selective delivery of cargo to targeted cells. Using polylactic-co-glycolic acid (PLGA), we recently highlighted the importance of polymer composition in the biological fate of the nanodrug delivery systems. However, the route of internalization of polymeric nanoparticles (NPs) is another key component to consider in the elaboration of modern and targeted devices. For that purpose, herein, we effectively synthesized and characterized glutathione-functionalized PLGA-nanoparticles (GSH-NPs) loaded with curcumin (GSH-NPs-Cur), using thiol-maleimide click reaction and determined their physicochemical properties. We found that GSH functionalization did not affect the drug loading efficiency (DLE), the size, the polydispersity index (PDI), the zeta potential, the release profile, and the stability of the formulation. While being nontoxic, the presence of GSH on the surface of the formulations exhibits a better neuroprotective property against acrolein. The neuronal internalization of GSH-NPs-Cur was higher than free curcumin. In order to track the intracellular localization of the formulations, we used a covalently attached rhodamine (PLGA-Rhod), into our GSH-functionalized matrix. We found that GSH-functionalized matrix could easily be taken up by neuronal cells. Furthermore, we found that GSH conjugation modifies the route of internalization enabling them to escape the uptake through macropinocytosis and therefore avoiding the lysosomal degradation. Taken together, GSH functionalization increases the uptake of formulations and modifies the route of internalization toward a safer pathway. This study shows that the choice of ideal ligand to develop NPs-targeting devices is a crucial step when designing innovative strategy for neuronal cells delivery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it