Hyaluronic acid targeted metal organic framework based on iron (III) for delivery of platinum curcumin cytotoxic agent to triple negative breast cancer cell line
Bibliographic record
Abstract
Platinum‐based drugs are an essential class of chemotherapy drugs in breast cancer. However, suffer from side effects and drug resistance; structural changes and applying drug carriers can lead to new drug candidates in this class. The metal‐organic frameworks (MOFs) as porous carriers have recently attracted much attention in drug delivery. In this study, for the first time, hyaluronic acid (HA)‐targeted MOF (NH 2 ‐MIL‐101 (Fe)) nanoparticles (Pt‐CUR@MIL@HA NPs) were evaluated in delivery of platinum‐curcumin (Pt‐CUR) prodrug to the MDA‐MB‐231 triple‐negative breast cancer cell line; in this regard, the cytotoxicity, ROS production, and cellular uptake of designed NPs have been studied. Different analysis methods confirmed chemical structures, the prepared NPs had a uniform morphology, and the hydrodynamic size of the optimized non‐targeted loaded particles increased to about 252 nm with a zeta potential of +26.9 mV, after targeting with HA, the size increased to 310 nm, and the zeta potential changed to −28 mV. Based on TGA and atomic absorption (ICP‐MS) results, the drug loading percent was determined to be about 30%–35%. Drug release from the HA targeted Pt‐CUR@MIL@HA system in the neutral condition was slow and sustained, and after 36 h, a maximum of 60% of the drug was released, but in acidic conditions, the release was increased, and by 18 h, the release was about 80%. The cytotoxicity of MOF NPs containing Pt‐CUR was more significant than that of the free drug, and HA targeted has resulted in more cellular uptake than the non‐targeted NPs. In conclusion, these new MOF‐ based HA‐coated NPs of PT‐CUR can be introduced to pre‐clinical researches after completing in vitro and in vivo studies.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 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 teacher head, 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".