Role of Antibody-Mediated Tumor Targeting and Route of Administration in Nanoparticle Tumor Accumulation in Vivo
Bibliographic record
Abstract
In this study, we have looked at enhancing tumor uptake and intracellular delivery of gold nanoparticles (AuNPs) while reducing the systemic exposure by systematic evaluation of the impact of targeting and route of administration on organ distribution. High-resolution microSPECT/CT imaging was used to track the in vivo fate of (111)In-labeled nontargeted and human epidermal growth factor receptor-2 (HER-2) targeted AuNPs following intravenous (i.v.) or intratumoral (i.t.) injection. For i.v. injection, the effects of GdCl3 (for deactivation of macrophages) and nonspecific (anti-CD20) antibody rituximab (for blocking of Fc mediated liver and spleen uptake) were studied. It was found that HER-2 targeting via attachment of trastuzumab paradoxically decreased tumor uptake as a result of faster elimination of the targeted AuNPs from the blood while improving internalization in HER-2-positive tumor cells as compared to nontargeted AuNPs. I.T. injections with HER-2 targeted AuNPs resulted in high tumor retention with low systemic exposure and represents an attractive delivery strategy. Our results provide a strategy for optimizing tumor delivery and quantifying organ distribution of this widely studied class of nanomaterial.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".