Integrated Machine Learning and Structure-Based Virtual Screening Identify Osimertinib as a TNIK Inhibitor for Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Traf2-and Nck-interacting kinase (TNIK) has been implicated in fibrosis-associated signaling pathways and has recently emerged as a promising therapeutic target for idiopathic pulmonary fibrosis (IPF). In this study, we employed an integrated strategy combining machine learning-based prediction and structure-based virtual screening to repurpose drugs from the DrugBank database as potential TNIK inhibitors for IPF treatment. Using this approach, we identified 19 candidate compounds, among which 14 demonstrated TNIK enzymatic inhibition rates exceeding 70% at a concentration of 10 μM, as determined by the ADP-Glo assay. Notably, among these candidates, the approved drug osimertinib showed potent TNIK inhibitory activity with an IC 50 of 151.90 nM and demonstrated an acceptable cytotoxicity profile in human lung fibroblast MRC-5 cells (CC 50 = 4366.01 nM). Furthermore, osimertinib significantly suppressed TGF-β1-induced fibrogenesis in human lung fibroblast-derived MRC-5 cells at 3 μM, as confirmed by qPCR and Western blot analyses. Molecular dynamics simulations and structural analyses revealed that osimertinib engages the ATP-binding pocket of TNIK via hinge hydrogen bonding with Cys108, while unoccupied subpockets near Met105 and the involvement of Gln157 provide opportunities for rational modifications to improve affinity and selectivity. These findings demonstrate the robustness of our integrated machine learning and structure-based virtual screening pipeline and suggest that osimertinib warrants further evaluation as a TNIK-targeted agent for IPF, with future studies needed to optimize its potency and selectivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".