iPSC–endothelial cell phenotypic drug screening and in silico analyses identify tyrphostin-AG1296 for pulmonary arterial hypertension
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a progressive disorder leading to occlusive vascular remodeling. Current PAH therapies improve quality of life but do not reverse structural abnormalities in the pulmonary vasculature. Here, we used high-throughput drug screening combined with in silico analyses of existing transcriptomic datasets to identify a promising lead compound to reverse PAH. Induced pluripotent stem cell-derived endothelial cells generated from six patients with PAH were exposed to 4500 compounds and assayed for improved cell survival after serum withdrawal using a chemiluminescent caspase assay. Subsequent validation of caspase activity and improved angiogenesis combined with data analyses using the Gene Expression Omnibus and Library of Integrated Network-Based Cellular Signatures databases revealed that the lead compound AG1296 was positively associated with an anti-PAH gene signature. AG1296 increased abundance of bone morphogenetic protein receptors, downstream signaling, and gene expression and suppressed PAH smooth muscle cell proliferation. AG1296 induced regression of PA neointimal lesions in lung organ culture and PA occlusive changes in the Sugen/hypoxia rat model and reduced right ventricular systolic pressure. Moreover, AG1296 improved vascular function and BMPR2 signaling and showed better correlation with the anti-PAH gene signature than other tyrosine kinase inhibitors. Specifically, AG1296 up-regulated small mothers against decapentaplegic (SMAD) 1/5 coactivators, cAMP response element-binding protein 3 (CREB3), and CREB5: CREB3 induced inhibitor of DNA binding 1 and downstream genes that improved vascular function. Thus, drug discovery for PAH can be accelerated by combining phenotypic screening with in silico analyses of publicly available datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".