Trial reportNature biotechnology2025
A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.
Trial report in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05154240 (A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Oral Single and Multiple Ascending Doses, Parallel Group Study to Evaluate the Safety, Tolerability, and Pharmacokinetics of INS018_055 in Healthy Subjects), which is not on this map. Cited by 151 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.
A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Oral Single and Multiple Ascending Doses, Parallel Group Study to Evaluate the Safety, Tolerability, and Pharmacokinetics of INS018_055 in Healthy Subjects
Who cites it
151 citing papers in PubMed, 1 synthesis or guideline pooled it, 237 citations in OpenAlex.
- 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
- A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial.Nature medicine · 2025Trial
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- De Novo Discovery of Nonstandard Thioisoindole-Bridged Bicyclic Peptides Targeting Traf2- and NCK-Interacting Kinase.Angewandte Chemie (International ed. in English) · 2026Article
- Integrating Genomics and Proteomics to Identify Causal Proteins and Biologic Pathways for Incident Atrial Fibrillation in CKD.Journal of the American Society of Nephrology : JASN · 2026Article
- 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
- Extracellular Matrix Remodeling in Tumors: Forces Beyond Fibroblasts.Cancer research · 2026Article
- KLF4/MLL3 complex axis drives NRBP2 transcription to eliminate acute myeloid leukemia cells.Leukemia · 2026Article
- Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges.International journal of molecular sciences · 2026Review
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- Intensification versus combination: revisiting the past and envisioning the future of LDHA inhibitors.Cancer chemotherapy and pharmacology · 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
- A combined artificial intelligence-wet lab approach identifies a pivotal role of the NADAlzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Next-Generation Sequencing in Pulmonary Fibrosis: Translational Promise and Current Clinical Limitations.Current issues in molecular biology · 2026Review
- Unleashing innovative cross-organ fibrosis therapies by harnessing the omics revolution.JCI insight · 2026Review
- ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.Journal of cheminformatics · 2026Article
91 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
30 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.
Indexed as
Identifiers
What OpenQuestion holds
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.