Targeted Codelivery of Prodigiosin and Simvastatin Using Smart BioMOF: Functionalization by Recombinant Anti-VEGFR1 scFv
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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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".