Increasing potency and safety of anticancer drugs through sortilin receptor-mediated cancer therapy: A new targeted approach for the treatment of ovarian cancer.
Bibliographic record
Abstract
e17068 Background: The development of personalized therapies against ovarian cancer remains highly challenging in current modern oncology. One strategy to achieve greater selectivity and better anticancer drug delivery into cancer cells is to conjugate cytotoxic agents to specific peptide ligands that selectively target receptors abundantly and/or exclusively expressed on these cells. Increased expression of Sortilin, a scavenging receptor, has been clinically observed in invasive ovarian cancer biopsies, and correlated with tumor grades. In light of this, we developed a peptide conjugation platform and a new Sortilin receptor-mediated vectorization strategy to increase cell targeting selectivity and cell delivery efficacy of anticancer agents. Methods: As a proof-of-concept, Doxorubicin was conjugated to a Sortilin binding peptide (KA-peptide). In vitro, intracellular delivery of the Doxorubicin-KA-peptide conjugate (DoxKA) was assessed in ES-2 and SKOV-3 ovarian cancer cell line models using flow cytometry and fluorescent microscopy. Sortilin gene silencing was performed with specific siRNA. DoxKA efficacy and safety were evaluated in vivo using ES-2 (CD1 nude mice) and SKOV-3 (athymic mice) subcutaneous xenograft models. Results: Uptake of DoxKA was observed in both Sortilin-positive ovarian cancer cell lines tested and was reduced when Sortilin expression was specifically silenced or upon competition with the Sortilin ligands Neurotensin and Progranulin. Results indicate that the uptake of DoxKA occurs via Sortilin-mediated endocytosis in contrast to simple diffusion for Doxorubicin. DoxKA was found to bypass the P-glycoprotein (P-gp) efflux pump in MDCK-MDR1 cells overexpressing P-gp as the uptake of DoxKA was unaffected by the P-gp inhibitor Cyclosporin A. In vivo, DoxKA showed lower potential side effects than Doxorubicin alone did, with decreased accumulation in healthy tissues such as heart and ovary. DoxKA caused a more potent inhibition of human ovarian tumor xenografts growth in mice and was better tolerated (absence of leukopenia and neutropenia) than the unconjugated Doxorubicin at an equivalent dose. Conclusions: These results strongly support the future clinical use of this platform to generate novel personalized therapeutics with specific targeting of Sortilin-positive tumors in the next stage of development in phase 1 clinical trial.
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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) | 0.001 | 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".