Nature Biotechnology | Insilico's AI-Driven IPF Candidate Rentosertib Shows Potential for Biological Age Reversal, as Assessed by Six Proteomic Aging Clocks
- 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., Sept. 7, 2026 /PRNewswire/ -- 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.
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 and Additional Information
"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.
