Accumulation of liposomes in metastatic tumor sites is not necessary for anti-cancer drug efficacy
Bibliographic record
Abstract
Abstract Background The tumor microenvironment is profoundly heterogeneous particularly when comparing sites of metastases. Establishing the extent of this heterogeneity may provide guidance on how best to design lipid-based drug delivery systems to treat metastatic disease. Building on our previous research, the current study employs a murine model of metastatic cancer to explore the distribution of ~ 100 nm liposomes. Methods Female NCr nude mice were inoculated with a fluorescently labeled, Her2/neu-positive, trastuzumab-resistant breast cancer cell line, JIMT-1mkate, either in the mammary fat pad to create an orthotopic tumor (OT), or via intracardiac injection (IC) to establish tumors throughout the body. Animals were dosed with fluorescent and radio-labeled liposomes. In vivo and ex vivo fluorescent imaging was used to track liposome distribution over a period of 48 h. Liposome distribution in orthotopic tumors was compared to sites of tumor growth that arose following IC injection. Results A significant amount of inter-vessel heterogeneity for DiR distribution was observed, with most tumor blood vessels showing little to no presence of the DiR-labelled liposomes. Further, there was limited extravascular distribution of DiR liposomes in the perivascular regions around DiR-positive vessels. While all OT tumors contained at least some DiR-positive vessels, many metastases had very little or none. Despite the apparent limited distribution of liposomes within metastases, two liposomal drug formulations, Irinophore C and Doxil, showed similar efficacy for both the OT and IC JIMT-1mkate models. Conclusion These findings suggest that liposomal formulations achieve therapeutic benefits through mechanisms that extend beyond the enhanced permeability and retention effect.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".