The VR23 Antitumor Compound Also Shows Strong Anti-Inflammatory Effects in a Human Rheumatoid Arthritis Cell Model and Acute Lung Inflammation in Mice
Bibliographic record
Abstract
We previously found that the novel VR23 proteasome inhibitor not only possesses an effective antitumor activity without causing any ill effects to animals but also reduces side effects caused by a partner drug when used in combination. In this article, we report that VR23, unlike other proteasome inhibitors, exhibits potent anti-inflammatory activity. In the LPS-induced THP-1 monocyte model, VR23 downregulates proinflammatory cytokines IL-1β, TNF-α, IL-6, and IL-8 at a similar efficacy to dexamethasone. In contrast, two well-known proteasome inhibitors, bortezomib and carfilzomib, do not effectively downregulate these proinflammatory cytokines. Data from a study with SW982 synovial cell line and primary human synoviocytes showed that VR23 not only effectively downregulates IL-6 but also inhibits cell migration. Interestingly, the IL-6 downregulation by VR23 was significantly more pronounced in the primary synovial cells from rheumatoid arthritis patients than those from healthy donors, suggesting that VR23 can be selective against rheumatoid arthritis. Finally, VR23 effectively reduces neutrophil migration, TNF-α secretion, and tissue inflammation in mice (female BALB/c strain) with an LPS-induced acute lung injury. Thus, our current data indicate that VR23 can be effective on both acute and chronic inflammatory conditions. Taken together with our previous work, VR23 is not only effective on inflammatory conditions but also applicable to different aspects of cancer control, including the treatment and prevention of tumor development by chronic inflammatory responses.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".