Surface peptide functionalization of zeolitic imidazolate framework-8 for autonomous homing and enhanced delivery of chemotherapeutic agent to lung tumor cells
Bibliographic record
Abstract
Chemotherapeutic agents used in treating certain cancer types operate in a non-selective manner tending to accumulate in normal, healthy tissue when high doses are used. To mitigate the toxicity effect resulting from this, there is an urgent need to develop active nano delivery systems capable of regulating optimal doses specifically to cancer cells without harming adjacent normal cells. Herein, we report a versatile nanoparticle - zeolitic imidazolate framework-8 (nZIF-8) - that is loaded with a chemotherapeutic agent (gemcitabine; GEM) and surface-functionalized with an autonomous homing system (Arg-Gly-Asp peptide ligand; RGD) via a straightforward, one-pot solvothermal reaction. Successful functionalization of the surface of nZIF-8 loaded GEM (GEM⊂nZIF-8) with RGD was proven by spectroscopic and electron microscopy techniques. This surface-functionalized nanoparticle (GEM⊂RGD@nZIF-8) exhibited enhanced uptake in human lung cancer cells (A549), compared with non-functionalized GEM⊂nZIF-8. The GEM⊂RGD@nZIF-8, experienced not only efficient uptake within A549, but also induced obvious cytotoxicity (75% at a concentration of 10 μg mL-1) and apoptosis (62%) after 48 h treatment when compared to the nanoparticle absent of the RGD homing system (GEM⊂nZIF-8). Most importantly, this surface-functionalized nanoparticle was more selective towards lung cancer cells (A549) than normal human lung fibroblast cells (MRC-5) with a selectivity index (SI) of 3.98. This work demonstrates a new one-pot strategy for realizing a surface-functionalized zeolitic imidazolate framework that actively targets cancer cells via an autonomous homing peptide system to deliver a chemotherapeutic payload effectively.
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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) | 0.000 | 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 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".