Targeted Nanoparticle for Co‐delivery of HER2 siRNA and a Taxane to Mirror the Standard Treatment of HER2<b>+</b> Breast Cancer: Efficacy in Breast Tumor and Brain Metastasis
Bibliographic record
Abstract
The first-line treatment of advanced and metastatic human epidermal growth factor receptor type 2 (HER2+) breast cancer requires two HER2-targeting antibodies (trastuzumab and pertuzumab) and a taxane (docetaxel or paclitaxel). The three-drug regimen costs over $320,000 per treatment course, requires a 4 h infusion time, and has many adverse side effects, while achieving only 18 months of progression-free survival. To replace this regimen, reduce infusion time, and enhance efficacy, a single therapeutic is developed based on trastuzumab-conjugated nanoparticles for co-delivering docetaxel and siRNA against HER2 (siHER2). The optimal nanoconstruct has a hydrodynamic size of 100 nm and specifically treats HER2+ breast cancer cells over organ-derived normal cells. In a drug-resistant orthotopic HER2+ HCC1954 tumor mouse model, the nanoconstruct inhibits tumor growth more effectively than the docetaxel and trastuzumab combination. When coupled with microbubble-assisted focused ultrasound that transiently disrupts the blood brain barrier, the nanoconstruct inhibits the growth of trastuzumab-resistant HER2+ BT474 tumors residing in the brains of mice. The nanoconstruct has a favorable safety profile in cells and in mice. Combination therapies have become the cornerstone of cancer treatment and this versatile nanoparticle platform can co-deliver multiple therapeutic types to ensure that they reach the target cells at the same time to realize their synergy.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".