Intracellular Targeted Nanocapsules Containing Nanobiotherapeutic Suppress Lung, Liver, Breast and Cervix Cancer Cell Lines by Prodrug Activation or Removal of Intracellular Tyrosine
Bibliographic record
Abstract
Background: Many cancer cell lines, such as Hepa 1-6 (liver), A549 (lung), Hela (cervical), and MCF7 (breast), do not overexpress tyrosinase, an enzyme needed to activate the prodrug quercetin into its active form, o-quinone. In addition, these cancers do not rely on extracellular tyrosine for growth, as they can produce small amounts intracellularly. Methods: We investigate two therapeutic strategies using nanocapsules containing polyhemoglobin–tyrosinase (PolyHb–Tyr–nano) for action on (1) the intracellular activation of quercetin to o-quinone and (2) the depletion of intracellular tyrosine. We applied these strategies to the four cell lines listed above. Results: (1) PolyHb–Tyr–nano activates quercetin intracellularly, increasing o-quinone levels and reducing cancer cell viability. (2) PolyHb–Tyr–nano alone suppresses tumor growth by lowering intracellular tyrosine. Furthermore, PolyHb–Tyr–nano shows selective cytotoxicity, with an LD50 of 0.7808 mg/mL in Hepa 1-6, compared with an extrapolated LD50 of 84,181 mg/mL in the normal liver cells. In contrast, quercetin activation results in an LD50 of 2.73 mg/mL in Hepa 1-6 and 74.18 mg/mL in normal hepatocytes. Conclusions: PolyHb–Tyr–nano offers dual therapeutic functions: (1) quercetin prodrug activation and (2) intracellular tyrosine depletion.
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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".