Core‐Shell Reactor Partitioning Enzyme and Prodrug by ZIF‐8 for NADPH‐Sensitive <i>In Situ</i> Prodrug Activation
Bibliographic record
Abstract
Abstract Enzyme‐prodrug therapies have shown unique advantages in efficiency, selectivity, and specificity of in vivo prodrug activation. However, precise spatiotemporal control of both the enzyme and its substrate at the target site, preservation of enzyme activity, and in situ substrate depletion due to low prodrug delivery efficiency continue to be great challenges. Here, we propose a novel core–shell reactor partitioning enzyme and prodrug by ZIF‐8, which integrates an enzyme with its substrate and increases the drug loading capacity (DLC) using a prodrug as the building ligand to form a Zn‐prodrug shell. Cytochrome P450 (CYP450) is immobilized in ZIF‐8, and the antitumor drug dacarbazine (DTIC) is coordinated and deposited in its outer layer with a high DLC of 43.6±0.8 %. With this configuration, a much higher prodrug conversion efficiency of CYP450 (36.5±1.5 %) and lower IC50 value (26.3±2.6 μg/mL) are measured for B16‐F10 cells with a higher NADPH concentration than those of L02 cells and HUVECs. With the tumor targeting ability of hyaluronic acid, this core–shell enzyme reactor shows a high tumor suppression rate of 96.6±1.9 % and provides a simple and versatile strategy for enabling in vivo biocatalysis to be more efficient, selective, and safer.
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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".