Cytotoxicity and Genotoxicity of Azobenzene-Based Polymeric Nanocarriers for Phototriggered Drug Release and Biomedical Applications
Bibliographic record
Abstract
photoisomerization upon UV irradiation. Polymeric photoresponse nanoparticles (PPNPs) based on azobenzene compounds and biopolymers such as chitosan derivatives show prospects of photodelivering drugs into cells with accelerated kinetics, enhancing their therapeutic effect. PPNP biocompatibility studies detect the safe concentrations for their administration and reduce the chance of side effects, improving the effectiveness of a potential treatment. Here, we report on a PPNP biocompatibility evaluation of viability and the first genotoxicity study of azobenzene-based PPNPs. Cell line models from human ventricular cardiomyocytes (RL14), as well as mouse fibroblasts (NIH3T3) as proof of concept, were exposed to different concentrations of azobenzene-based PPNPs and their precursors to evaluate the consequences on mitochondrial metabolism (MTT assay), the number of viable cells (trypan blue exclusion test), and deoxyribonucleic acid (DNA) damage (comet assay). Lethal concentrations of 50 (LC50) of the PPNPs and their precursors were higher than the required drug release and synthesis concentrations. The PPNPs affected the cell membrane at concentrations higher than 2 mg/mL, and lower concentrations exhibited lesser damage to cellular genetic material. An azobenzene derivative functionalized with a biopolymer to assemble PPNPs demonstrated biocompatibility with the evaluated cell lines. The PPNPs encapsulated Nile red and dofetilide separately as model and antiarrhythmic drugs, respectively, and delivered upon UV irradiation, proving the phototriggered drug release concept. Biocompatible PPNPs are a promising technology for fast drug release with high cell interaction opening new opportunities for azobenzene biomedical 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) | 0.000 | 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) | 0.001 | 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".