Identification of a potent natural triterpenoid inhibitor of proteosome chymotrypsin-like activity and NF-κB with antimyeloma activity in vitro and in vivo
Bibliographic record
Abstract
As multiple myeloma tumors universally dysregulate cyclin D genes we conducted high-throughput chemical library screens for compounds that induce suppression of cyclin D2 promoter transcription. The top-ranked compound was a natural triterpenoid, pristimerin. Strikingly, the early transcriptional response of cells treated with pristimerin closely resembles cellular responses elicited by proteosome inhibitors, with rapid induction of heat shock proteins, activating transcription factor 3 (ATF3), and CHOP. Enzymatic assays and immunoblotting confirm that pristimerin rapidly (< 90 minutes) and specifically inhibits chymotrypsin-like proteosome activity at low concentrations (< 100 nM) and causes accumulation of cellular ubiquitinated proteins. Notably, cytotoxic triterpenoids including pristimerin inhibit NF-kappaB activation via inhibition of IKK alpha or IKK beta, whereas proteosome inhibitors instead suppress NF-kappaB function by impairing degradation of ubiquitinated I kappaB. By inhibiting both IKK and the proteosome, pristimerin causes overt suppression of constitutive NF-kappaB activity in myeloma cells that may mediate its suppression of cyclin D. Multiple myeloma is exquisitely sensitive to proteosome or NF-kappaB pathway inhibition. Consistent with this, pristimerin is potently and selectively lethal to primary myeloma cells (IC(50) < 100 nM), inhibits xenografted plasmacytoma tumors in mice, and is synergistically cytotoxic with bortezomib--providing the rationale for pharmaceutical development of triterpenoid dual-function proteosome/NF-kappaB inhibitors as therapeutics for human multiple myeloma and related malignancies.
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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 | 0.000 |
| 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".