A High Throughput Screen Identifies Nefopam as Targeting Cell Proliferation in β-Catenin Driven Neoplastic and Reactive Fibroproliferative Disorders
Bibliographic record
Abstract
Fibroproliferative disorders include neoplastic and reactive processes (e.g. desmoid tumor and hypertrophic scars). They are characterized by activation of β-catenin signaling, and effective pharmacologic approaches are lacking. Here we undertook a high throughput screen using human desmoid tumor cell cultures to identify agents that would inhibit cell viability in tumor cells but not normal fibroblasts. Agents were then tested in additional cell cultures for an effect on cell proliferation, apoptosis, and β-catenin protein level. Ultimately they were tested in Apc1638N mice, which develop desmoid tumors, as well as in wild type mice subjected to full thickness skin wounds. The screen identified Neofopam, as an agent that inhibited cell numbers to 42% of baseline in cell cultures from β-catenin driven fibroproliferative disorders. Nefopam decreased cell proliferation and β-catenin protein level to 50% of baseline in these same cell cultures. The half maximal effective concentration in-vitro was 0.5 uM and there was a plateau in the effect after 48 hours of treatment. Nefopam caused a 45% decline in tumor number, 33% decline in tumor volume, and a 40% decline in scar size when tested in mice. There was also a 50% decline in β-catenin level in-vivo. Nefopam targets β-catenin protein level in mesenchymal cells in-vitro and in-vivo, and may be an effective therapy for neoplastic and reactive processes driven by β-catenin mediated signaling.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".