Targeted Codelivery of Prodigiosin and Simvastatin Using Smart BioMOF: Functionalization by Recombinant Anti-VEGFR1 scFv
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biological metal-organic frameworks (BioMOFs) are hybrid compounds in which metal nodes are linked to biocompatible organic ligands and have potential for medical application. Herein, we developed a novel BioMOF modified with an anti-VEGFR1 scFv antibody (D16F7 scFv). Our BioMOF is co-loaded with a combination of an anticancer compound and a lipid-lowering drug to simultaneously suppress the proliferation, growth rate and metastases of cancer cells in cell culture model system. In particular, Prodigiosin (PG) and Simvastatin (SIM) were co-loaded into the newly synthesized Ca-Gly BioMOF nanoparticles coated with maltose and functionalized with a recombinant maltose binding protein-scFv fragment of anti-VEGFR1 (Ca-Gly-Maltose-D16F7). The nanoformulation, termed PG + SIM-NP-D16F7, has been shown to have strong active targeting behavior towards VEGFR1-overexpresing cancer cells. Moreover, the co-delivery of PG and SIM not only effectively inhibits the proliferation of cancer cells, but also prevents their invasion and metastasis. The PG + SIM-NP-D16F7 nanocarrier exhibited stronger cytotoxic and anti-metastatic effects compared to mono-treatment of free drugs and drug-loaded nanoparticles. Smart co-delivery of PG and SIM on BioMOF nanoparticles had synergistic effects on growth inhibition and prevented cancer cell metastasis. The present nanoplatform can be introduced as a promising tool for chemotherapy compared with mono-treatment and/or non-targeted formulations.
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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) | 0.000 | 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 it