H-Ferritin nanoparticle-mediated delivery of antibodies across a BBB <i>in vitro</i> model for treatment of brain malignancies
Bibliographic record
Abstract
Brain cancers are a group of neoplasms that can be either primary, such as glioblastoma multiforme (GBM), or metastatic, such as the HER2+ breast cancer brain metastasis. The brain represents a sanctuary for cancer cells thanks to the presence of the blood brain barrier (BBB) that controls trafficking of molecules, protecting the brain from toxic substances including drugs. Considering that GBM and HER2+ breast cancer brain metastases are characterized by EGFR and HER2 over-expression respectively, CTX- and TZ-based treatment could be effective. Several studies show that these monoclonal antibodies (mAbs) exert both a cytostatic activity interfering with the transduction pathways of EGFR family and a cytotoxic activity mainly through the immune system activation via the antibody dependent cell-mediated cytotoxicity (ADCC). Since the major limitation to therapeutic mAbs application is the presence of the BBB, here we use a recombinant form of human apoferritin (HFn) as a nanovector to promote the delivery of mAbs to the brain for the activation of the ADCC response. Using a transwell model of the BBB we proved the crossing ability of HFn-mAb. Cellular uptake of HFn-mAb by human cerebral microvascular endothelial cells (hCMEC/D3) was demonstrated by confocal microscopy. Moreover, after crossing the endothelial monolayer, HFn-conjugated mAbs retain their biological activity against targets, as assessed by MTS and ADCC assays. Our data support the use of HFn as efficient carrier to enhance the BBB crossing of mAbs, without affecting their antitumoral activity.
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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".