Xanthatin nanocrystals exert anti-inflammatory properties against TNFα-primed 2D monolayers and in 3D spheroids of human HT29 colorectal cancer cells
Bibliographic record
Abstract
Poor water-solubility of emerging new chemotherapeutic drugs lead to low absorption and tissue bioavailability. Improved drug delivery has therefore recently been achieved through the versatile physico-chemical properties of nanocrystals (NCs) in targeted cancer therapies. Here, nanocrystalization was used with xanthatin, a not highly water-soluble natural sesquiterpene lactone compound that possesses anti-tumour properties and which was recently investigated for potential use in the treatment of cancer and autoimmune diseases. Given that tumour-promoting inflammation is a hallmark of colorectal cancer (CRC), and that epidemiological studies associated inflammatory biomarkers to CRC poor prognosis and therapy resistance, the anti-inflammatory properties of xanthatin NCs were assessed in 2D monolayers and in 3D spheroids of a human HT29 CRC cell model. The 3D spheroids being a model recapitulating a cancer stem cells and chemoresistant phenotype. HT29 2D monolayer cell response was first tested against four pro-inflammatory inducers including phorbol-12-myristate-13-acetate, tumour necrosis factor alpha (TNFα), transforming growth factor beta, and Concanavalin A. Of these inducers, HT29 cell response to TNFα resulted in the most elevated expression of cyclooxygenase (COX)-2 which was prevented by commercial xanthatin along with the phosphorylation of the extracellular signal-regulated kinase 1/2 and of IkappaB (IκB). Alteration of 3D spheroids formation and of the inflammatory/immunity transcriptomic signature was also found better altered by xanthatin NCs in comparison to commercial xanthatin and the isolated molecule. Collectively, our data indicate that xanthatin nanocrystallization did not alter the potential in vitro anti-inflammatory and anticancer properties of xanthatin against a 3D CRC chemoresistance cellular model. These properties make NCs a significant advancement in the field of cancer theranostics to improve patient outcomes.
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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) | 0.001 | 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".