Nature Biotechnology | Insilico’s AI-Driven IPF Candidate Rentosertib Shows Potential for Biological Age Reversal, as Assessed by Six Proteomic Aging Clocks https://t.co/8Q7R3O3ImU

First-in-Class Evaluation of an AI-Driven Candidate: This is the first de-novo drug designed with generative AI for a de-novo target discovered using AI and Aging Research (AI aging clocks, AI models and Hallmarks of Aging Assessment).

First Universal Consensus Across Six Independently-Developed Proteomic Aging Clocks: Analyzing 12-week longitudinal Olink proteomic data from 42 IPF patients, six aging clock models (including ProtAge, OrganAge, and PAC) independently developed by the leading groups from Harvard, Oxford, PKU, and Insilico, unanimously indicated that rentosertib reversed predicted biological age.

First Scalable Blueprint for Dual-Purpose Clinical Trials: The study is the first demonstration of a novel drug for a novel target discovered using aging research purposed to the age-related disease with aging biomarkers applied in a context of a clinical study. It establishes a standardized framework for embedding geroscience endpoints into standard disease trials, aligned with FDA Biomarker/BEST guidelines for dual-purpose aging and disease therapeutics.

CAMBRIDGE, Mass. — Monday, September 7, 2026 (5AM EST) — Insilico Medicine (“Insilico”), a clinical-stage company focused on generative AI-driven drug discovery and development, today announced the publication of a new study conducted in collaboration with an international team of scientists from Harvard Medical School, Stanford University, The Broad Institute, RWTH Aachen University, Peking University and Westlake University.

Using longitudinal Olink proteomic data (CNCB OMIX accession: OMIX008341) collected during a Phase IIa clinical trial of rentosertib, researchers evaluated biological age using six internationally recognized, independently developed proteomic aging clocks. All six models consistently indicated a reduction in predicted biological age among patients treated with rentosertib. In addition, the Forced Vital Capacity (FVC), the essential measure of lung function declining with age, showed promising dose-dependent reversal compared to placebo aligning with the proteomic aging clock age reversal.

Recently published in Nature Biotechnology, the study represents the first clinical evaluation of a potentially first-in-class drug candidate featuring both an AI-discovered target with relevance to aging and disease biology and an AI-designed molecule, rather than a repurposed generic drug such as rapamycin or metformin. The findings also provide a proof-of-concept framework for integrating aging biomarkers into standard clinical trials to accelerate the discovery of geroprotective drugs and longevity therapeutics.

The results will be presented by first author Alex Zhavoronkov, founder and CEO of Insilico Medicine, at the Nature conference: Redefining Healthcare in the Age of AI, to be held at Sorbonne University in Paris on September 8, 2026.

From aging biology to a clinical drug

Unlike traditional geroscience studies that rely on repurposed drugs like rapamycin or metformin, Insilico pursued a different approach by incorporating aging biology into its drug discovery strategy from the outset.
Using its AI-powered target discovery platform, Insilico identified TNIK as a novel target implicated in aging and fibrosis and performed hallmarks of aging assessment where TNIK scored highly in 6 hallmarks. TNIK was subsequently prioritized as a dual-purpose target relevant to both underlying aging biology and idiopathic pulmonary fibrosis (IPF). Insilico’s generative chemistry platform, Chemistry42, was then deployed to design the small-molecule candidate rentosertib targeting TNIK. The program progressed from target identification to preclinical candidate nomination in approximately 18 months, with the preclinical findings published in Nature Biotechnology in 2024.

In June 2025, Insilico reported the results of a Phase IIa trial of Rentosertib for IPF (NCT05938920) in Nature Medicine. The trial met its primary safety endpoint and demonstrated a promising dose-dependent trend in efficacy. One of the common efficacy outcome measures is Forced Vital Capacity (FVC), the essential measure of lung function. The typical age of onset of IPF is around 65 years. Interestingly, in healthy individuals older than 65, FVC declines as well typically at the rate of 20-50 mL/year and may also serve as a potential physiological biomarker of aging. In the previous publication, it was revealed that in the 60-mg once-daily group, patients experienced a mean improvement in forced vital capacity (FVC) of +98.4 mL, compared with a mean decline of -20.3 mL in the placebo group (-62.3 excluding outlier).

Crucially, the trial protocol prospectively included longitudinal serum proteomic screening for exploratory biomarker analyses, which proved consistent with the drug’s proposed anti-fibrotic and anti-inflammatory activity. All data collected from the trial were subsequently deposited with the China National Center for Bioinformation (CNCB), enabling the current investigation of biological aging biomarkers.

Six different clocks point to strong biological age reversal measured by proteomics

In the current study, researchers evaluated serum proteome profiles from 42 trial participants across 2,841 proteins. The team applied six independently developed proteomic aging clocks, including ProtAge, OrganAge (chronological and mortality variants), PAC, ipfP3GPT, and PAOPAC. Despite relying on diverse methodologies and training criteria, all six clocks consistently exhibited a trend toward reversed biological age among rentosertib-treated patients compared to placebo. The peak effect is observed at Week 4 in participants receiving 30 mg BID with approximately 3–4 years reversal in biological age and up to 6 years in a certain aging clock.
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Rentosertib decreases predicted biological age across various regimens as measured by most aging clocks, most significantly in the 30 mg BID group and in week 4.
Beyond changes in biological age scores, the study showed that rentosertib’s anti-aging effects operate partially independent of its respiratory benefits. Notably, the dose associated with the greatest improvement in lung function differed from that producing the strongest age-reversal signal, suggesting that the drug’s geroprotective activity was not merely a downstream consequence of disease improvement.

This systemic impact was further validated through comparisons with 55,319 UK Biobank profiles, which showed that rentosertib directly reversed the typical age-related protein-expression trajectories. Mechanistically, the drug acted as a senomorphic agent, suppressing key drivers of cellular senescence, including EREG, ESM1, IGFBP4, ITGA2, MMP10, MMP13, and SPP1 while downregulating growth-factor signaling pathways associated with accelerated aging, including RTK–PI3K and RAS–ERK, and modulating antioxidant and cholesterol metabolisms.

A framework for the future

To support reproducibility and further research, all research data have been deposited at CNCB (accession OMIX008341), and the underlying pipeline code has been published as the open-source Python library on GitHub.
Beyond these specific findings, the study lays out a scalable blueprint for embedding geroscience endpoints into disease-focused drug development. The research proposes a stepwise framework: collecting aging and senescence biomarkers prospectively as exploratory endpoints in disease trials, and ultimately pursuing biomarker qualification or composite clinical endpoints under the FDA Biomarker Qualification Program and FDA–NIH BEST framework. Such an approach could surface geroprotective candidates years or even decades earlier than the traditional path of post-approval drug repurposing.

Insilico’s dual-purpose therapeutic strategy is also proving its viability as a sustainable commercial model. Recently, Insilico reported total revenue of approximately $106 million in the first half of 2026, a 287% year-over-year increase, and achieved its first profitable half-year since listing, with an adjusted net profit exceeding $51 million. This milestone was driven by a series of out-licensing, co-development, and R&D collaborations with global partners, including Eli Lilly, Servier, Takeda, SK Biopharmaceuticals, Qilu Pharmaceutical, Hygtia Therapeutics, CMS, and Tenacia. As of the latest practicable date, the total contract value of transactions announced by Insilico in 2026 reached approximately $7.3 billion, pushing the cumulative contract value of its major collaborations since 2021 approximately $11 billion.

On the AI-driven R&D front, Insilico nominated nine development candidates within nine months of 2026 as of late August, setting a new company record for annual pipeline productivity and achieving eight clinical milestones across its proprietary and co-developed programs. Leading this progress is rentosertib (ISM001-055), the world's first drug candidate discovered and developed using generative AI, which has advanced to a Phase III trial evaluating for idiopathic pulmonary fibrosis (IPF). In addition, Insilico Medicine launched a comprehensive set of benchmarks that allow foundation models to be evaluated in all tasks needed for drug discovery and launched state of the art (SOTA) foundation models outperforming other models and even internal tools in benchmarks. Using this new capability Insilico hopes to expand and accelerate longevity drug discovery and development both in terms of scale, therapeutic modalities, and indications.

Industry Commentary

"Six proteomic clocks from six independent groups, applied to the same 42 patients, all reported a younger biological age in the treated arms. What convinces me is not the size of the effect but the agreement, because these models share neither their features nor their training data.” said Michael Levitt, PhD, 2013 Nobel Prize laureate in Chemistry. “This trial cannot yet separate slower aging from a treated lung, and the authors say so plainly. The experiment in healthy volunteers is the one I want to see next."
“Insilico Medicine was built with one purpose – to develop a credible and sustainable business model for the development of frontier deep learning technology and its application to practical aging research and drug discovery with the purpose of developing using AI and practical discovery and development of longevity therapeutics. This first program demonstrates the first proof of concept for the novel dual-purpose therapeutic targeting aging but proposed toward the age-related disease discovered in record time making exploratory development in multiple indications commercially viable.

Today, the majority of Insilico’s 40+ drug programs have dual purpose targeting aging and disease in a broad range of therapeutic areas. By pursuing a credible biotechnology business model, the company reached profitability and established the longevity board, which is rare for the publicly-traded frontier biotechnology company. I am also happy that the drug carries the name of the true hero of the AI drug discovery revolution, our co-CEO, Dr. Ren.

The social and economic opportunity for drugs reversing biological age is vast - trillions of dollars and billions of life years. If you add just 3 extra years to everyone’s lifespan, that translates into roughly 25 billion life years on the global scale - at current life expectancy, that is roughly 340 million human lifetimes. More lifetimes than humanity lost in all the wars ever fought. If you manage to add 3 years to everyone’s life, the drug should be able to significantly extend the healthy portion of life as well translating into trillions of dollars in productivity and savings”, said Alex Zhavoronkov, PhD, founder and co-CEO of Insilico Medicine.

“In this study, we evaluated clinical trial data to compare the blood proteomic profiles of pulmonary fibrosis patients treated with Rentosertib versus placebo”, said Ludger Goeminne, Research Fellow in Medicine at Harvard Medical School, and co-author of the paper. “Using six proteomic aging clocks including models our team published in Cell Metabolism, we observed significant reductions in predicted biological age across multiple organ-specific clocks, and further pathway analysis confirmed that Rentosertib’s biological impact extends far beyond merely reducing fibrosis.”

“As the developer of PAOPAC (Proteome-Aware Organ Proxy Aging Clock), I am thrilled to see our tool applied to the analysis of the Phase IIa clinical trial for rentosertib. Dr. Zhavoronkov and his colleagues are the first to systematically evaluate a novel drug candidate using multiple proteomic aging clocks within a single clinical trial cohort. The methodologies underlying these clocks range from traditional machine learning (OrganAge, PAOPAC, PAC) to deep learning (ProtAge, ipfP3GPT), with training targets encompassing both chronological age (ProtAge, OrganAgechrono, ipfP3GPT, PAOPAC) and mortality risk (PAC, OrganAgemortality). Across the board, these clocks consistently predicted a reduction in biological age within the rentosertib treatment group. This cross-model consistency demonstrates that rentosertib's effect on aging-related proteomic signals is not a model-specific artifact, but rather a highly robust biological phenomenon.”, said Professor Jing-Dong Jackie Han, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), China. “Furthermore, this study establishes a relatively comprehensive workflow for the standardized application of aging clocks in clinical trials, covering cohort establishment, omics profiling, multi-clock parallel analysis, result cross-validation, and mechanistic interpretation. It also introduces an innovative practical approach to clinical trial design: evaluating aging biomarkers in parallel during trials targeting specific indications, particularly age-related diseases. This strategy holds great promise for accelerating the discovery and validation of longevity interventions.

Finally, my congratulations on the breakthrough achieved with rentosertib—an innovative drug discovered in China and clinically evaluated primarily in Chinese patient cohorts. As a domestic Chinese research team behind the development of aging clocks, we look forward to witnessing more industry-academia-research collaborations driving future clinical trials in aging interventions.”

Reference

[1] Zhavoronkov, A., Galkin, F., Chen, S. et al. Integration of proteomic aging clocks in a phase 2a clinical trial supports simultaneous geroprotective assessment. Nat Biotechnol (2026). https://t.co/zsdLOCHZBB
[2] Pun, F. W., Leung, G. H. D., Leung, H. W. et al. Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine. Aging (Albany NY) 14, 2475–2506 (2022). https://t.co/lJYo0f51dP

[3] Tang, Q., Xiao, D., Veviorskiy, A. et al. AI-Driven Robotics Laboratory Identifies Pharmacological TNIK Inhibition as a Potent Senomorphic Agent. Aging Dis 17, 432–451 (2026). https://t.co/YCVDJkqdcy

[4] Ren, F., Aliper, A., Chen, J. et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat Biotechnol 43, 63–75 (2025). https://t.co/OZo0KkgioZ

[5] Xu, Z., Ren, F., Wang, P. et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat Med 31, 2602–2610 (2025). https://t.co/sDuZn5pNlJ

About Rentosertib (ISM001-055)

Rentosertib is a potentially first-in-class small molecule targeting TNIK, developed using Insilico Medicine's generative AI platform https://t.co/bgUHHWS3Ss. TNIK was identified through AI-powered target discovery as a gene involved in six hallmarks of aging, making it a dual-purpose target for both aging biology and idiopathic pulmonary fibrosis (IPF). The drug's discovery, design, preclinical validation, and Phase I results were published in Nature Biotechnology in March 2024. Phase IIa results were published in Nature Medicine in June 2025. Rentosertib has entered Phase III clinical development in China.
About Idiopathic Pulmonary Fibrosis (IPF)

Idiopathic Pulmonary Fibrosis is a chronic, scarring lung disease characterized by progressive and irreversible decline in lung function. Affecting approximately 5 million people worldwide, IPF carries a poor prognosis with a median survival of 3–4 years. Current approved treatments can slow disease progression but cannot stop or reverse it, leaving a significant unmet need for 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 https://t.co/2Xs4csfDtn.
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 https://t.co/YVad2a93X5