• Potentially first-in-class oral TNIK inhibitor advances into late-stage development after milestones marked by vital peer-reviewed papers: the discovery-to-clinic journey in Nature Biotechnology, and positive Phase IIa clinical results in Nature Medicine
• Rentosertib represents a novel mechanism, novel AI-identified target, and novel AI-empowered molecule emerging from Insilico's aging-biology-driven Pharma.AI platform
• The GENESIS-IPF Phase IIa study showed manageable safety and tolerability, with the 60 mg once-daily arm demonstrating mean forced vital capacity improvement of +98.4 mL at 12 weeks

CAMBRIDGE, Mass., July 7, 2026 /PRNewswire/ -- Insilico Medicine ("Insilico," HKEX: 3696), a clinical-stage biotechnology company powered by generative AI, today announced the initiation of the Phase III clinical trial for Rentosertib, its potentially first-in-class oral small-molecule inhibitor targeting TNIK for the treatment of idiopathic pulmonary fibrosis (IPF), a progressive, age-related fibrotic lung disease with high unmet medical need.

Rentosertib, formerly known as ISM001-055 / INS018_055, was discovered and designed through Insilico's Pharma.AI platform. The program combines a novel fibrosis target prioritized by Biology42: PandaOmics, Insilico's AI-powered biology engine, with a novel small molecule generated and optimized through Chemistry42, Insilico's generative chemistry platform. Insilico leverages PandaOmics for indication prioritization and Medicine42's inClinico platform to predict and improve the program's clinical trial outcomes. The program's discovery-to-clinic path was published in Nature Biotechnology, while randomized Phase IIa clinical results were published in Nature Medicine and presented at the American Thoracic Society (ATS) 2025 International Conference.

The initiation of Rentosertib's Phase III clinical trial marks a major late-stage milestone for AI-driven drug discovery: a medicine whose target was identified with AI, whose chemical structure was designed with generative AI, and whose clinical development is aimed at a severe age-related disease in which current approved antifibrotic therapies can slow progression but do not reverse the degenerative course of disease.

To evaluate Rentosertib in this next stage of development, the upcoming Phase III clinical trial is a prospective, randomized, double-blind, placebo-controlled, parallel-group Phase III study. It will be led by Professor Zuojun Xu of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences as the Leading Principal Investigator (Leading PI), with Academician Nanshan Zhong of the Chinese Academy of Engineering, a renowned expert in respiratory medicine, and President Chang Chen of Shanghai Pulmonary Hospital serving as Co-Leading Principal Investigators (Co-Leading PIs). The study is expected to enroll 320 patients with idiopathic pulmonary fibrosis (IPF) and is designed to systematically evaluate the efficacy and safety of once-daily Rentosertib administered over 52 weeks.

As Leading PI of the study, Professor Zuojun Xu from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, commented: "Interstitial lung disease (ILD) has always been a key focus of research. From basic science to disease mechanisms and potential therapeutics, research in this area is continuously improving and has achieved several encouraging clinical-stage breakthroughs. In the Phase IIa trial of Rentosertib, we were pleased to observe a dose-dependent efficacy trend, which attracted widespread attention and anticipation from research institutions globally. For the Phase III study at a larger scale with longer duration, we look forward to enhanced collaboration across all parties regarding study standards, risk mitigation, and cross-center data consistency, so as to realize an objective and comprehensive evaluation of Rentosertib, benefiting patients in need."

IPF is a chronic, progressive lung-scarring disease that disproportionately affects older adults. As fibrosis accumulates, lung tissue becomes stiff and scarred, making breathing increasingly difficult and leading to irreversible decline in lung function. The median survival after diagnosis is commonly reported at approximately two to four years, and there remains a substantial need for disease-modifying treatments that can meaningfully alter the clinical course.

TNIK is a serine/threonine kinase implicated in fibrosis-driving and inflammation-related pathways including Wnt, TGF-β, Hippo/YAP-TAZ, JNK and NF-κB signaling. Insilico identified TNIK as a high-priority fibrosis target using PandaOmics by integrating multi-omics data from fibrotic tissues, biological network analysis, causal inference, pathway analysis, literature and patent intelligence, and aging-relevant target scoring. In the Nature Biotechnology paper, TNIK was reported as the top-ranked candidate in the protein and receptor kinase discovery scenario, representing a previously underexplored target class for IPF compared with the receptor tyrosine kinase biology addressed by existing antifibrotic drugs.

"IPF is one of the clearest clinical examples of an age-related disease in which fibrosis, chronic inflammation, extracellular matrix remodeling and cellular senescence intersect," said Feng Ren, PhD, Co-CEO and Chief Scientific Officer of Insilico Medicine. "Rentosertib was not discovered by starting from a conventional target and simply screening more compounds. It came from a biology-first, aging-informed AI workflow that connected TNIK to fibrotic and inflammatory disease mechanisms, and then used generative chemistry to create a drug candidate with the properties required for clinical development."

A program built from aging biology

Rentosertib also reflects Insilico's long-running thesis that aging biology can serve as a discovery engine for diseases of aging. In Aging, Insilico and collaborators described a hallmarks-of-aging-based strategy for identifying dual-purpose disease and age-associated targets using PandaOmics. The approach proposed that targets implicated in multiple hallmarks of aging, inflammation, extracellular matrix remodeling and age-related disease mechanisms could provide a route to therapeutics that address both disease pathology and aging-associated biology.

This thesis was later highlighted in Nature Aging in the research highlight "Drug discovery by AI trained on aging biology", which described Insilico's use of PandaOmics to analyze multi-omics IPF datasets, biological networks and scientific literature, and to apply hallmarks-of-aging assessment in the prioritization of fibrosis targets. The highlight described TNIK as a top candidate emerging from an AI system built to connect disease biology with aging-relevant mechanisms.

The aging-related rationale for TNIK inhibition has continued to develop. In Aging and Disease, Insilico and collaborators reported that pharmacological TNIK inhibition showed senomorphic activity in cellular senescence models using an AI-driven automated laboratory. The study identified TNIK inhibition as a potent senomorphic strategy and reported reductions in aging-related markers including senescence-associated secretory phenotype (SASP) and extracellular matrix remodeling signals across senescence models. These findings do not establish Rentosertib as an anti-aging therapy, but they strengthen the scientific rationale for investigating TNIK at the intersection of fibrosis, inflammation, senescence and age-related disease biology.

Peer-reviewed discovery-to-clinic evidence

The discovery and early development of Rentosertib were described in the Nature Biotechnology paper "A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models". The paper reported the application of PandaOmics for fibrosis target discovery, the identification of TNIK as an AI-prioritized target, Chemistry42-driven small-molecule design and optimization, anti-fibrotic activity in preclinical models, and Phase I clinical evidence supporting safety, tolerability and pharmacokinetics in humans.

The medicinal chemistry foundation of the program was further described in the Journal of Medicinal Chemistry paper "Discovery of Bis-imidazolecarboxamide Derivatives as Novel, Potent, and Selective TNIK Inhibitors for the Treatment of Idiopathic Pulmonary Fibrosis". The paper reported the discovery of novel TNIK inhibitor chemotypes and structure-guided medicinal chemistry, including structural support from the TNIK kinase domain co-crystal structure. Together, the Nature Biotechnology and Journal of Medicinal Chemistry papers provide unusually detailed peer-reviewed documentation for an AI-originated clinical-stage program: not only the platform narrative, but the target biology, chemistry, pharmacology and translational package.

In 2025, Nature Medicine published Phase IIa results in the paper "A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial". The GENESIS-IPF trial was a multicenter, double-blind, randomized, placebo-controlled Phase IIa study in 71 patients with IPF across 22 sites in China. Patients were randomized to receive placebo, Rentosertib 30 mg once daily (QD), Rentosertib 30 mg twice daily (BID), or Rentosertib 60 mg QD for 12 weeks.

The study met its primary safety and tolerability objective, with treatment-emergent adverse event rates similar across treatment arms. Secondary and exploratory analyses showed a dose-dependent lung-function signal. In the 60 mg QD arm, patients demonstrated a mean forced vital capacity (FVC) change of +98.4 mL at 12 weeks, compared with -20.3 mL in the placebo group. Exploratory biomarker analyses further supported Rentosertib's anti-fibrotic and anti-inflammatory mechanism, including changes in profibrotic and inflammatory proteins consistent with TNIK pathway modulation.

"The Phase IIa results gave us the confidence to advance Rentosertib into larger and longer clinical testing," said Carol Satler, MD, PhD, Senior Vice President for Clinical Development, Non-Oncology at Insilico Medicine. "The Phase III study is designed to determine whether the safety profile and lung-function signal observed in Phase IIa can translate into clinically meaningful benefit for patients with IPF. IPF remains a devastating disease, and a therapy with a differentiated mechanism would be an important addition to the field."

Professor Chang Chen, Co-Leading PI of the study and President of Shanghai Pulmonary Hospital, stated: "This will be a landmark clinical study for the industry. After seeing Rentosertib's potential to control and even reverse the progression of IPF, we are witnessing the process of AI-driven drug discovery moving from concept to validation. By combining AI technology with China innovation, we could achieve the leap from R&D to clinical application and ultimately, to real changes in patients' lives. As clinicians, we understand the challenges faced by patients, so participating in and advancing this process means a lot."

A novel mechanism, target and molecule for an age-related fibrotic disease

Rentosertib is differentiated by three layers of novelty. First, the program is built around a novel mechanism for IPF centered on TNIK inhibition and its role in fibrotic and inflammatory signaling networks. Second, TNIK was identified and prioritized as a disease target through AI-enabled analysis of fibrosis and aging biology rather than selected as a conventional, heavily pursued IPF target. Third, Rentosertib itself is a novel small molecule generated and optimized through Insilico's Chemistry42 platform, with the medicinal chemistry disclosed in peer-reviewed literature.

This end-to-end path is important for the broader pharmaceutical industry because many AI drug discovery efforts have focused on accelerating known workflows, identifying hits against known targets, or improving individual steps in discovery. Rentosertib represents a more ambitious model: use AI to generate a disease hypothesis, identify a novel target, design a novel molecule, validate the biology experimentally, and advance the program through human clinical testing.

"Rentosertib is a very important program for Insilico because it represents the full arc of our mission: using AI not only to move faster, but to originate new biology, new chemistry, and new therapeutic opportunities in aging and disease," said Alex Zhavoronkov, PhD, Founder and Chief Executive Officer of Insilico Medicine. "This program began with the hypothesis that aging biology could help identify powerful targets for major diseases and aging itself. It has now advanced through target discovery, molecular design, preclinical validation, Phase I safety, randomized Phase IIa clinical data, and into Phase III development. For the AI drug discovery field, this is no longer only a speed story. It is a testament to the ability of AI to create truly novel therapeutics with novel target, novel molecule, not just discover me-better molecules for known targets".

Insilico's growing pipeline productivity

Rentosertib, the lead program in Insilico's AI-driven pipeline, has become the company's first asset to enter a Phase III clinical trial, underscoring the maturity of its end-to-end AI-enabled drug discovery and development model. Beyond Rentosertib, Insilico has continued to demonstrate strong pipeline productivity, with 31 preclinical candidate (PCC) nominations, 13 of them received IND clearances and 8 of them ongoing Phase I trials.

By integrating AI with automation, Insilico is setting a new benchmark for preclinical drug development efficiency. While traditional early-stage drug discovery typically takes 2.5 to 4 years, Insilico has consistently reached preclinical candidate (PCC) nomination in an average of just 12 to 18 months, with only 60 to 200 molecules synthesized and tested per program.

At the same time, Insilico is further strengthening the underlying performance of its AI systems. Since 2014, the company has built an end-to-end Pharma.AI platform that integrates Biology42 for disease modeling and target discovery, Chemistry42 for generative molecule design, Medicine42 for translational and clinical development support, and Science42 for scientific research. Leveraging extensive internal experience and datasets, the company has distilled thousands of benchmarks and integrated them into MMAI Gym. Serving as both a "trainer and benchmark" for scientific AI, MMAI Gym enables organizations to train models for domain-specific reasoning while rigorously evaluating their performance on real-world tasks, advancing the path toward pharma superintelligence. To date, Human Longevity and Liquid AI have collaborated with Insilico, joining as partners of MMAI Gym.

About the Phase III Clinical Trial

The Phase III clinical trial will evaluate Rentosertib in patients with idiopathic pulmonary fibrosis. The prospective, randomized, double-blind, placebo-controlled, parallel-group Phase III study is expected to enroll a total of 320 participants across 47 centers in China.  The primary endpoint is the annual rate of decline in forced vital capacity (FVC) over 52 weeks. The Key secondary endpoint is time to first occurrence of any disease progression event. The trial is intended to assess whether Rentosertib can provide clinically meaningful benefit in a larger patient population and over a longer treatment period than the Phase IIa study. While the initiation of the Phase III trial represents an important milestone in the development of Rentosertib, the drug remains investigational and has not been approved by any regulatory authority.

About Rentosertib

Rentosertib is a potentially first-in-class small molecule targeting TNIK developed utilizing generative AI. In IPF, the activation of TNIK drives pathological fibrosis in the lungs, contributing to the progressive decline in lung function. By inhibiting TNIK, Rentosertib aims to halt or reverse fibrotic processes, offering a disease-modifying treatment for patients with IPF. The U.S. Food and Drug Administration granted Orphan Drug Designation to Rentosertib for IPF in February 2023. The program's discovery-to-clinic development was published in  Nature Biotechnology, its Phase IIa clinical results were published in Journal of Medicinal Chemistry, and its medicinal chemistry was published in the Journal of Medicinal Chemistry.

About Idiopathic Pulmonary Fibrosis (IPF)

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, scarring lung disease characterized by a progressive and irreversible decline in lung function. Affecting approximately 5 million people worldwide, IPF carries a poor prognosis, with a median survival of 3 to 4 years. Current approved treatments, including antifibrotic drugs, can slow disease progression but do not stop or reverse it, leaving a significant unmet need for more effective, disease-modifying therapies.

About Insilico Medicine

Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend healthy longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, under the stock code 03696.HK.

By integrating AI and automation technologies and deep in-house drug discovery capabilities, Insilico is delivering innovative drug solutions for unmet needs including fibrosis, oncology, immunology, pain, and obesity and metabolic disorders. Additionally, Insilico extends the reach of Pharma. AI across diverse industries, such as advanced materials, agriculture, nutritional products and veterinary medicine.

For more information, please visit www.insilico.com

Key supporting publications and materials

Ren F. et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nature Biotechnology. doi.org/10.1038/s41587-024-02143-0. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nature Medicine. doi.org/10.1038/s41591-025-03743-2. Pun F.W. et al. Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine. Aging (Albany NY). doi.org/10.18632/aging.203960. Thuault S. Drug discovery by AI trained on aging biology. Nature Aging. doi.org/10.1038/s43587-024-00615-1. AI-Driven Robotics Laboratory Identifies Pharmacological TNIK Inhibition as a Potent Senomorphic Agent. Aging and Disease. doi.org/10.14336/AD.2024.1492. Discovery of Bis-imidazolecarboxamide Derivatives as Novel, Potent, and Selective TNIK Inhibitors for the Treatment of Idiopathic Pulmonary Fibrosis. Journal of Medicinal Chemistry. doi.org/10.1021/acs.jmedchem.4c01580.

 

Source: Insilico Medicine Related Stocks: HongKong:3696