SREKA-targeted liposomes for highly metastatic breast cancer therapy
Bibliographic record
Abstract
Chemotherapy is still a leading therapeutic approach in various tumor types that is often accompanied by a poor prognosis because of metastases. PEGylated liposomes with CREKA targeting moiety are well-known therapeutic agents, especially in highly metastatic experimental models. CREKA specifically targets tumor-associated ECM, which is present at the primary, as well as metastatic tumor sites. To better understand the function of the targeting moieties, we decided to design various liposome formulations with different amounts of targeting moiety attached to their DSPE-PEG molecules. Moreover, a new tumor-homing pentapeptide (SREKA) was designed, and a novel conjugation strategy between SREKA and DSPE-PEGs. First, the in vitro proliferation inhibition of drug-loaded liposomes and the cellular uptake of their cargo were investigated. Afterward, liposome stability in murine blood and drug accumulation in different tissues were measured. Furthermore, in vivo tumor growth, and metastasis inhibition potencies of the different liposome formulations were examined. According to our comparative studies, SREKA-liposomes have a uniform phenotype after formulation and have similar characteristics and tumor-homing capabilities to CREKA-liposomes. However, the exchange of the N-terminal cysteine to serine during conjugation results in a higher production yield and better stability upon conjugation to DSPE-PEGs. We also showed that SREKA-liposomes have significant inhibition on primary tumor growth and metastasis incidence; furthermore, increase the survival rate of tumor-bearing mice. Besides, we provide evidence that the amount of targeting moiety attached to DSPE-PEGs is largely responsible for the stability of liposomes, therefore it plays an important role in toxicity and targeting.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".