AI-designed Drug Rentosertib Advances Into Phase III Trial for Idiopathic Pulmonary Fibrosis

Insilico Medicine has initiated a Phase III clinical trial of Rentosertib, an investigational oral TNIK inhibitor for idiopathic pulmonary fibrosis (IPF) discovered and designed using its AI-powered drug discovery platform. The advancement makes Rentosertib one of the most advanced AI-originated drug candidates to reach late-stage clinical development, representing a critical milestone in evaluating whether generative AI can translate novel biological insights into clinically meaningful therapies.

The Phase III initiation follows positive Phase IIa clinical results and peer-reviewed publications in Nature Medicine and Nature Biotechnology highlighting Rentosertib’s clinical evaluation and Insilico’s AI-driven discovery approach. Unlike conventional AI applications that primarily focus on improving existing drug discovery processes, Insilico developed Rentosertib through an integrated workflow spanning AI-powered target identification, generative chemistry, preclinical validation, and clinical development.

Phase IIa Results Propel Rentosertib Into Late-Stage Development

The Phase III trial follows the successful completion of the GENESIS-IPF Phase IIa study, which evaluated Rentosertib in 71 patients across 22 clinical sites in China. The study met its primary safety and tolerability objectives, with manageable adverse events observed across treatment groups. At 12 weeks, patients receiving the 60 mg once-daily dose showed a mean forced vital capacity (FVC) improvement of 98.4 mL from baseline, while patients receiving placebo experienced a decline of 20.3 mL.

FVC is a key clinical measurement in IPF trials, as progressive loss of lung function is closely associated with disease progression. The observed separation between the Rentosertib and placebo groups provided early evidence supporting further evaluation in a larger and longer Phase III study.

“ 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.”

How AI Identified TNIK as a Novel IPF Drug Target

IPF is a chronic and progressive lung disease characterized by irreversible fibrosis, abnormal extracellular matrix remodeling, chronic inflammation, and cellular senescence. Because IPF predominantly affects older adults, researchers have increasingly explored the relationship between aging biology and fibrotic diseases.

Rentosertib targets TNIK, a serine/threonine kinase involved in fibrosis-associated signaling pathways. The target was identified through Insilico’s AI-powered biology platform PandaOmics, which integrates multi-omics datasets, disease biology information, scientific literature, and aging-related biological signatures.

Unlike traditional drug discovery strategies that often begin with established targets and optimize compounds through screening, Insilico applied a biology-first AI approach to identify TNIK as a potential therapeutic target for IPF and subsequently used generative chemistry technology to design a drug candidate with properties suitable for clinical development.

“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.”

Aging Biology Provides a New Framework for AI-Driven Drug Discovery

A key differentiator of Rentosertib’s development is the integration of aging biology into the drug discovery process. As global populations age, diseases such as IPF, neurodegenerative disorders, and metabolic conditions are increasingly recognized as being influenced by fundamental aging mechanisms, including cellular senescence, chronic inflammation, and impaired tissue repair.

Insilico’s approach integrates aging-related biological insights with AI-driven analysis to explore potential disease mechanisms and identify new therapeutic opportunities in age-associated diseases. By combining aging-related insights with large-scale biological datasets, AI platforms may enable researchers to identify novel therapeutic targets that would be difficult to discover through conventional approaches alone.

Phase III Trial Will Evaluate Long-Term Impact on IPF Progression

The Phase III study is a randomized, double-blind, placebo-controlled trial designed to enroll 320 patients with IPF across 47 clinical centers in China.

Participants will receive once-daily Rentosertib or placebo for 52 weeks. The primary endpoint is the annual rate of decline in forced vital capacity (FVC), a widely accepted measure for assessing IPF disease progression. Professor Zuojun Xu from Peking Union Medical College Hospital serves as the Leading Principal Investigator, while Academician Nanshan Zhong and President Chang Chen serve as Co-Leading Principal Investigators.

Zuojun Xu noted that the Phase III study will provide an opportunity to further evaluate Rentosertib’s efficacy, safety profile, and consistency of clinical outcomes across multiple research centers. “This will be a landmark clinical study for the industry,” said Professor Chang Chen, Co-Leading Principal Investigator of the study and President of Shanghai Pulmonary Hospital.

“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.”

Why AI-designed Drug Rentosertib Matters for the Future of Drug Discovery

The advancement of Rentosertib into Phase III represents an important test for the broader AI drug discovery field. Over the past decade, artificial intelligence has increasingly been adopted across pharmaceutical research, from target identification and molecular screening to compound optimization. However, the ultimate benchmark for AI platforms is whether they can generate novel medicines capable of progressing through rigorous clinical development.

Rentosertib represents one of the most advanced examples of an AI-originated therapeutic program reaching late-stage clinical testing. Its Phase III results could provide important evidence on whether AI can contribute not only to faster drug discovery but also to the creation of differentiated therapies targeting previously unexplored biological mechanisms.

Insilico Continues to Expand Its AI Drug Discovery Pipeline

Beyond Rentosertib, Insilico continues to expand its AI-enabled drug discovery pipeline through its proprietary Pharma.AI platform, which integrates Biology42 for AI-powered target discovery, Chemistry42 for generative molecular design, and Medicine42 for clinical development applications.

According to the company, Insilico’s pipeline includes 31 preclinical candidates, and its AI platform can accelerate preclinical candidate identification within approximately 12 to 18 months, significantly shortening traditional drug discovery timelines.

“Rentosertib represents the full arc of our mission: using AI not only to move faster, but to create new biology, new chemistry, and new therapeutic opportunities,” said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine.

“This program has progressed from AI-driven target discovery and molecular design through preclinical validation, Phase I safety evaluation, randomized Phase IIa clinical data, and now Phase III development. For the AI drug discovery field, this is no longer only a story about speed—it demonstrates the potential of AI to generate novel therapeutic approaches and develop medicines against previously unexplored biological mechanisms.”

Author

Steven Chung

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