Development of EGFR-targeted evodiamine nanoparticles for the treatment of colorectal cancer
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Invasion and metastasis of colorectal cancer (CRC) are leading causes of death of CRC patients. Previous findings demonstrate that evodiamine (Evo), an indolequinone alkaloid, is effective in combating CRC; however, its poor aqueous solubility and low oral bioavailability limit its application in the prevention of invasion and metastasis of CRC. It is known that selectively targeting cancer-specific receptors highly expressed on the surface of cancer cells by nanocarriers loaded with cytotoxic drugs is a viable strategy in nanobiotechnology to enhance cancer cell killing and minimize side effects. In this study, we report the development of a new class of nanotherapeutics: EGFR-targeting Evo-encapsulated poly(amino acid) nanoparticles (GE11-Evo-NPs). These nanoparticles exhibited good aqueous solubility, slow release, and active targeting capability. Their inhibitory effect on human colon cancer cells and therapeutic efficacy against invasion and metastasis of CRC in nude mice were systematically investigated. Mechanisms of the GE11-Evo-NPs against EGFR mediated invasion and metastasis of CRC were also explored. Compared with free Evo, the GE11-Evo-NPs showed significantly increased cytotoxicity to colon cancer cells and potently inhibited CRC LoVo cell adhesion, invasion, and migration. The expression of EGFR, VEGF, and MMP proteins was dramatically down-regulated, which may partially account for their inhibition of invasion and metastasis of CRC. Moreover, in vivo studies show that the GE11-Evo-NPs exhibited much greater potency than other control groups in inhibiting CRC invasion and metastasis, tumor volume, and growth in nude mice, leading to a significantly prolonged tumor-bearing survival duration (P < 0.01).
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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) | 0.000 | 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 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".