Mitigation of Tacrolimus-Associated Nephrotoxicity by PLGA Nanoparticulate Delivery Following Multiple Dosing to Mice while Maintaining its Immunosuppressive Activity
Bibliographic record
Abstract
Abstract The aim of this study was to assess the ability of PLGA nanoparticles (NPs) to reduce the tacrolimus (TAC)-associated nephrotoxicity following multiple dose administration. The mean diameter of prepared NPs was in the range of 227 to 263 nm with an 8.32% drug loading (w/w). Moreover, in vitro release profile of TAC-loaded NPs showed a sustained release of the drug with only less than 30% release within 12 days. Flow cytometry as well as fluorescence microscopy results confirmed the uptake of FITC-labelled PLGA NPs by dendritic cells. The ex vivo study showed that TAC-loaded NPs caused a significant suppression of the proliferation of CD4+ and CD8+ cells, which was comparable to the control formulation (Prograf). In vivo immunosuppressive activity as well as the kidney function were assessed following drug administration to mice. The animals received TAC subcutaneously at a daily dose of 1 mg/kg for 30 days delivered as the control formulation (Prograf) or TAC-loaded NPs. The results revealed significantly lower drug-associated toxicity with an activity comparable to Prograf for TAC-loaded PLGA NPs. These findings show a potential for PLGA NPs in reducing the nephrotoxicity of TAC while preserving the immunosuppressive activity.
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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 | 0.001 |
| 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".