Small molecule MIRA-1 induces in vitro and in vivo anti-myeloma activity and synergizes with current anti-myeloma agents
Bibliographic record
Abstract
BACKGROUND: Small molecule MIRA-1 induced mutant p53-dependent apoptosis in several types of solid tumours. However, anti-tumour activity of MIRA-1 in haematological malignancies including multiple myeloma (MM) is unknown. In this study, we evaluated the effect of MIRA-1 in MM. METHODS: We examined the anti-tumour activity of MIRA-1 alone or in combination with current anti-myeloma agents in a panel of MM cell lines, primary MM samples, and in a mouse xenograft model of MM. RESULTS: MIRA-1 treatment resulted in the inhibition of viability, colony formation, and migration and increase in apoptosis of MM cells irrespective of p53 status accompanied by upregulation of Puma and Bax and downregulation of Mcl-1 and c-Myc. Genetic knockdown of p53 did not abrogate apoptotic response of MIRA-1. MIRA-1 triggered activation of PERK and IRE-α leading to splicing of XBP1 indicating an association of endoplasmic reticulum stress response. Furthermore, combined treatment of MIRA-1 with dexamethasone, doxorubicin or velcade displayed synergistic response in MM cells. Importantly, MIRA-1 alone or in combination with dexamethasone retarded tumour growth and prolonged survival without showing any untoward toxicity in the mice bearing MM tumour. CONCLUSIONS: Our data provide the preclinical framework for clinical evaluation of MIRA-1 as a novel therapeutic agent to improve patient outcome in MM.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".