FLECS Technology for High-Throughput Screening of Hypercontractile Cellular Phenotypes in Fibrosis: A Function-First Approach to Anti-Fibrotic Drug Discovery
Bibliographic record
Abstract
Abstract The pivotal role of myofibroblast contractility in the pathophysiology of fibrosis is widely recognized, yet HTS approaches are not available to quantify this critically important function in drug discovery. We develop, validate, and scale-up a HTS platform that quantifies contractile function of primary human lung myofibroblasts upon treatment with pro-fibrotic TGF-β1. With the fully automated assay we screened a library of 40,000 novel small molecules in under 80 h of total assay run-time. We identified 42 hit compounds that inhibited the TGF-β1-induced contractile phenotype of myofibroblasts, and enriched for 19 that specifically target myofibroblasts but not phenotypically related smooth muscle cells. Selected hits were validated in an ex vivo lung tissue models for their inhibitory effects on fibrotic gene upregulation by TGF-β1. Our results demonstrate that integrating a functional contraction test into the drug screening process is key to identify compounds with targeted and diverse activity as potential anti-fibrotic agents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".