Active Targeting of Block Copolymer Micelles with Trastuzumab Fab Fragments and Nuclear Localization Signal Leads to Increased Tumor Uptake and Nuclear Localization in HER2-Overexpressing Xenografts
Bibliographic record
Abstract
Block copolymer micelles (BCMs) have been employed as effective drug delivery systems to solid tumors by virtue of their capacity to transport large therapeutic payloads and passively target tumor sites. Active targeting of nanoparticles (NPs) has been exploited as a means to increase the therapeutic efficacy of NP-based drugs by promoting their delivery to cellular sites of action. Effective whole tumor accumulation and cellular uptake constitute key objectives in the success of preclinical drug formulations, although they have seldom been investigated concurrently in vivo. The current study aims to elucidate the in vivo fate of 31-nm-sized block copolymer micelles (BCMs) targeted to the nucleus of HER2-overexpressing breast cancer cells. Pharmacokinetics, biodistribution, tumor uptake, and intratumoral distribution of BCMs were investigated in mice bearing subcutaneous BT-474 and MDA-MB-231 xenografts expressing high and low levels of HER2, respectively. Radiolabeling with (111)indium enabled quantitative assessment of BCM distribution at the whole body, tissue, and cellular levels. Surface-grafted trastuzumab Fab fragments (TmAb-Fab) facilitated binding and internalization of BCMs by HER2-positive breast cancer cells, while synthetic 13-mer nuclear localization signal (NLS) peptides conjugated to the TmAb-Fab conferred nuclear translocation capability. Active targeting of BCMs led to a 5-fold increase in tumor uptake in HER2-overexpressing BT-474 tumors, alongside a correspondingly greater level of cellular uptake and nuclear localization, relative to the nontargeted formulations. This study distinctively highlights the quantitative evaluation of active targeting on tumor, cellular and subcellular uptake of BCMs and presents a promising platform for the effective delivery of chemo- and/or radiotherapy in vivo.
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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) | 0.001 | 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".