Multitargeted Nanoparticles Deliver Synergistic Drugs across the Blood–Brain Barrier to Brain Metastases of Triple Negative Breast Cancer Cells and Tumor‐Associated Macrophages
Bibliographic record
Abstract
Patients with brain metastases of triple negative breast cancer (TNBC) have a poor prognosis owing to the lack of targeted therapies, the aggressive nature of TNBC, and the presence of the blood-brain barrier (BBB) that blocks penetration of most drugs. Additionally, infiltration of tumor-associated macrophages (TAMs) promotes tumor progression. Here, a terpolymer-lipid hybrid nanoparticle (TPLN) system is designed with multiple targeting moieties to first undergo synchronized BBB crossing and then actively target TNBC cells and TAMs in microlesions of brain metastases. In vitro and in vivo studies demonstrate that covalently bound polysorbate 80 in the terpolymer enables the low-density lipoprotein receptor-mediated BBB crossing and TAM-targetability of the TPLN. Conjugation of cyclic internalizing peptide (iRGD) enhances cellular uptake, cytotoxicity, and drug delivery to brain metastases of integrin-overexpressing TNBC cells. iRGD-TPLN with coloaded doxorubicin (DOX) and mitomycin C (MMC) (iRGD-DMTPLN) exhibits higher efficacy in reducing metastatic burden and TAMs than nontargeted DMTPLN or a free DOX/MMC combination. iRGD-DMTPLN treatment reduces metastatic burden by 6-fold and 19-fold and increases host median survival by 1.3-fold and 1.6-fold compared to DMTPLN or free DOX/MMC treatments, respectively. These findings suggest that iRGD-DMTPLN is a promising multitargeted drug delivery system for the treatment of integrin-overexpressing brain metastases of TNBC.
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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".