The synthetic triterpenoid CDDO-Imidazolide suppresses experimental liver metastasis
Bibliographic record
Abstract
C21 The liver is a common site of metastasis, and while liver metastases often prove fatal, the metastatic process itself is inefficient. The majority of cells that leave the primary tumor do not survive to form metastatic tumors, with many cells lost in the secondary organ due to apoptosis. The inherent inefficiency of the metastatic process indicates that there are stages in the process at which cells are susceptible to apoptotic stimuli. The synthetic triterpenoid 1-[2-cyano-3-,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole (CDDO-Imidazolide, CDDO-Im) can be administered orally, resulting in high concentrations in the liver. CDDO-Im has been shown to inhibit proliferation or induce apoptosis (dose dependent) in several cancer cell lines in vitro. However, the effect of CDDO-Im on liver metastasis in vivo is not known. To assess the ability of CDDO-Im to inhibit metastasis, experimental liver metastasis models were used. B16F1 (mouse melanoma) and HT29 (human colon carcinoma) cells, which undergo apoptosis at nanomolar concentrations of CDDO-Im in vitro in cell culture, were injected via a surgically exposed mesenteric vein to target these tumor cells to the liver of mice. Mice were then treated with CDDO-Im (800 mg/kg diet) or control on an intermittent feeding program, such that treated mice received CDDO-Im at least 75% of the time. Livers were removed at endpoint and tumor burden determined by a novel whole liver magnetic resonance imaging (MRI) procedure, as well as by histology. It was found that oral treatment with CDDO-Im decreased liver tumor burden more than 50% in both B16F1 and HT29 experimental metastasis models. These studies demonstrate that MRI can be effectively used to quantify tumor burden from intact mouse livers. Our results suggest that CDDO-Im may have potential as a therapy for liver metastases.
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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) | 0.001 | 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) | 0.002 | 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".