Trial reportNature medicine2025
A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial.
Trial report in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative efficacy and safety of monotherapy and combination pharmacotherapies for idiopathic pulmonary fibrosis: a network meta-analysis of randomized controlled trials.BMC pulmonary medicine · 2026Pooled it
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- How to apply artificial intelligence (AI) to facilitate and enhance MASH trials.Hepatology international · 2026Review
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- Integration of proteomic aging clocks in a phase 2a clinical trial supports simultaneous geroprotective assessment.Nature biotechnology · 2026Article
- Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges.International journal of molecular sciences · 2026Review
- KLF4/MLL3 complex axis drives NRBP2 transcription to eliminate acute myeloid leukemia cells.Leukemia · 2026Article
- Transmembrane Protein PTCHD4 Is a Novel Regulator of Cellular Senescence and Age-Related Pathologies.Aging cell · 2026Article
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- Geroprotective Effects of Drugs Modulating Metabolic Pathways: Perspectives of Pharmacology in Anti-Aging Therapy.International journal of molecular sciences · 2026Review
- An Integrated Computational Workflow for Discovering Alkaloid-Derived Ligands of Cyclin-Dependent Kinase 2.Pharmaceuticals (Basel, Switzerland) · 2026Article
- New paradigm of q-CAR drug development targeting disease-specific protein conformations.npj drug discovery · 2026Review
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Advances in pharmacotherapy for fibrotic interstitial lung disease.Medical review (2021) · 2026Review
- Epidemiological Characteristics, Target Distribution, and Clinical Value of Targeted Anticancer Drugs Approved in China: A Cross-Sectional Study.Clinical pharmacology and therapeutics · 2026Article
- Machine Learning-driven Prediction of Cervical Cancer Cell Viability After Treatment With Thymoquinone, Curcumin, and 5-Fluorouracil.Applied biochemistry and biotechnology · 2026Article
- New approach methodologies for next-generation risk assessment of nanomaterials and nano-enabled products.Nano convergence · 2026Review
- Unleashing innovative cross-organ fibrosis therapies by harnessing the omics revolution.JCI insight · 2026Review
- ERS Congress 2025: highlights from the Interstitial Lung Diseases Assembly.ERJ open research · 2026Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite substantial progress in artificial intelligence (AI) for generative chemistry, few novel AI-discovered or AI-designed drugs have reached human clinical trials. Here we present the results of the first phase 2a multicenter, double-blind, randomized, placebo-controlled trial testing the safety and efficacy of rentosertib (formerly ISM001-055), a first-in-class AI-generated small-molecule inhibitor of TNIK, a first-in-class target in idiopathic pulmonary fibrosis (IPF) discovered using generative AI. IPF is an age-related progressive lung condition with no current therapies available that reverse the degenerative course of disease. Patients were randomized to 12 weeks of treatment with 30 mg rentosertib once daily (QD, n = 18), 30 mg rentosertib twice daily (BID, n = 18), 60 mg rentosertib QD (n = 18) or placebo (n = 17). The primary endpoint was the percentage of patients who have at least one treatment-emergent adverse event, which was similar across all treatment arms (72.2% in patients receiving 30 mg rentosertib QD (n = 13/18), 83.3% for 30 mg rentosertib BID (n = 15/18), 83.3% for 60 mg rentosertib QD (n = 15/18) and 70.6% for placebo (n = 12/17)). Treatment-related serious adverse event rates were low and comparable across treatment groups, with the most common events leading to treatment discontinuation related to liver toxicity or diarrhea. Secondary endpoints included pharmacokinetic dynamics (C
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.