【AI Explained】A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

Literature Information

TL;DR

This study identifies TRAF2- and NCK-interacting kinase (TNIK) as a novel anti-fibrotic target for idiopathic pulmonary fibrosis (IPF) and develops INS018_055, a small-molecule TNIK inhibitor, using an AI-driven approach, which shows promising anti-fibrotic and anti-inflammatory effects in vivo and favorable safety profiles in phase I clinical trials. The research underscores the potential of generative AI in accelerating drug discovery and developing effective therapies for IPF, a condition previously lacking successful treatment options.

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small-molecule TNIK inhibitor · idiopathic pulmonary fibrosis · anti-fibrotic · artificial intelligence · clinical trial

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.

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Primary Questions Addressed

1. What are the specific mechanisms by which TNIK inhibition affects fibrosis in different organs?
2. How does the pharmacokinetic profile of INS018_055 compare to other anti-fibrotic agents currently in development?
3. What were the key findings from the phase I clinical trials regarding the safety and tolerability of INS018_055?
4. Can the AI-driven methodology used in this study be applied to other therapeutic targets for fibrotic diseases?
5. What are the potential implications of INS018_055’s anti-inflammatory effects on the treatment of idiopathic pulmonary fibrosis?

Key Findings

Research Summary: A Small-Molecule TNIK Inhibitor Targets Fibrosis in Preclinical and Clinical Models

Research Background and Purpose

Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease with a high mortality rate and limited effective treatment options. Traditional drug targets have failed to translate into successful therapies. This study aims to identify TRAF2- and NCK-interacting kinase (TNIK) as a novel anti-fibrotic target using an artificial intelligence (AI)-driven approach and to develop INS018_055, a small-molecule TNIK inhibitor, to evaluate its efficacy and safety in preclinical and clinical models.

Major Methods/Materials/Experimental Design

The study utilized a comprehensive AI-driven drug discovery pipeline to identify TNIK as a target for fibrosis. The key steps involved:

1. Target Identification: The PandaOmics platform was employed to analyze multiomics datasets from IPF patients to identify potential targets, ranking TNIK as the top candidate based on various bioinformatics criteria.
2. Compound Design: INS018_055 was generated using generative chemistry techniques, focusing on optimizing drug-like properties and binding affinity to TNIK.
3. In Vitro and In Vivo Testing: The anti-fibrotic activity of INS018_055 was validated in various fibrosis models, including murine and rat models, as well as human cell lines.
4. Clinical Trials: A randomized, double-blind, placebo-controlled Phase I clinical trial was conducted (NCT05154240) involving 78 healthy participants to assess the safety and pharmacokinetics of INS018_055.

Key Elements for Clinical Trials:

• Study Design: Randomized, double-blind, placebo-controlled.
• Recruitment Criteria: Healthy volunteers aged 18-55.
• Intervention: Participants received either INS018_055 or placebo.
• Endpoints: Primary endpoint was safety; secondary endpoints included pharmacokinetics.

Key Results and Findings

• In Vitro: INS018_055 demonstrated potent anti-fibrotic effects by inhibiting TGF-β-induced pathways in human lung fibroblasts, significantly reducing α-SMA and fibronectin expression.
• In Vivo: In murine models of bleomycin-induced lung fibrosis, INS018_055 significantly improved lung function, reduced fibrotic area, and decreased collagen deposition compared to vehicle controls.
• Clinical Trials: Phase I trials showed that INS018_055 was well tolerated, with a favorable pharmacokinetic profile, supporting its potential for further clinical development.

Major Conclusions/Significance/Innovation

The study successfully identifies TNIK as a novel therapeutic target for fibrosis and demonstrates that INS018_055 is a promising anti-fibrotic agent with significant efficacy in preclinical models and a favorable safety profile in clinical trials. This work illustrates the potential of AI-driven drug discovery pipelines to accelerate the development of targeted therapies for complex diseases like fibrosis.

Research Limitations and Future Directions

While the results are promising, the study acknowledges several limitations:

• Phase I Trials: Limited to healthy volunteers; efficacy in IPF patients needs further validation.
• Long-Term Effects: Long-term safety and efficacy of INS018_055 in diverse patient populations remain to be established.

Future studies are planned to include Phase II trials for patients with IPF (NCT05975983 and NCT05938920), exploring the broader application of INS018_055 in other fibrotic diseases, such as chronic kidney disease and skin fibrosis. The ongoing development of AI-driven methodologies in drug discovery is also anticipated to enhance the identification of additional therapeutic targets.

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Literatures Citing This Work

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