Preclinical Efficacy of Tasquinimod in Myelodysplastic Neoplasms: Restoring Erythropoiesis and Mitigating Bone Loss
Bibliographic record
Abstract
Myelodysplastic neoplasms (MDSs) are clonal disorders characterized by ineffective hematopoiesis, dysplasia, and a risk of transformation into acute myeloid leukemia. MDS is also associated with a higher incidence of osteoporosis, suggesting a complex interplay between hematopoiesis, the bone marrow (BM) microenvironment, and bone homeostasis. Targeting inflammation has emerged as a promising therapeutic strategy, particularly in lower risk MDS. Tasquinimod (TASQ) is a small-molecule inhibitor of the inflammatory alarmin S100A9, blocking its interaction with TLR4 and RAGE receptors. We investigated the efficacy of TASQ in modulating inflammation and improving disease phenotype using in vitro and in vivo MDS models. Immunofluorescence staining of human BM identified neutrophils and macrophages as primary S100A9 sources. Exposure of mesenchymal stromal cells (MSCs) to S100A9 induced Toll-like receptor 4 (TLR4) downstream signaling, resulting in increased expression of IRAK1, NF-κB-p65, interleukin-1β (IL-1β), IL-18, caspase 1, and PD-L1. These effects were effectively abolished by TASQ. Additionally, TASQ restored the disturbed MSC-mediated hematopoietic support, as demonstrated by increased numbers of cobblestone area-forming cells and colony-forming units. In NHD13 MDS mice, TASQ (30 mg/kg, 12 weeks) improved hemoglobin and red blood cell counts, but exerted no effect in wild-type (WT) mice. Additionally, TASQ improved bone microarchitecture by increasing trabecular number and bone volume, likely a result of reduced osteoclast activity. Our findings suggest that TASQ mitigates inflammasome activation in the MDS BM, improving erythropoiesis and bone health. These results provide a necessary preclinical basis for clinical trials in lower risk MDS patients, in whom anemia and osteoporosis often coexist.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".