An AI-designed drug candidate known as rentosertib has demonstrated a reduction in biological aging markers across six independently developed proteomic aging clocks during human clinical testing.
From Generative Chemistry to Clinical Trial Design
Insilico Medicine developed rentosertib using its generative chemistry platform, Chemistry42, following an AI-driven target discovery process. Rather than starting with a repurposed generic medication like metformin or rapamycin, the company targeted TNIK, a protein identified as relevant to both aging biology and fibrosis. 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. The drug discovery program moved from target identification to preclinical candidate nomination in approximately 18 months, with preclinical findings published in Nature Biotechnology in 2024.
In June 2025, Insilico reported results from a Phase IIa clinical trial of rentosertib for idiopathic pulmonary fibrosis (IPF) in Nature Medicine. The phase 2a trial of rentosertib in IPF (NCT05938920) is a randomized, double-blind, placebo-controlled trial conducted in 2023–2024 across 21 locations in China that recruited male and female patients older than 40 years, with a confirmed IPF diagnosis and in stable condition. Out of 128 patients screened, 71 were selected to receive 30 mg rentosertib once daily (QD, N = 18), 30 mg rentosertib twice daily (BID, N = 18), 60 mg QD (N = 18) or placebo (N = 17). By the end of trial, 16 patients discontinued treatment.
Tracking Biological Age With Six Proteomic Clocks
According to the protocol, lung forced vital capacity (FVC) was recorded at the start and end of the trial (12 weeks). Blood samples were collected from the participants at start and end of the trial, as well as at the second and fourth weeks after the start. A total of 43 participants consented to serum proteomic screening, 1 of whom was excluded due to a missing measurement at the end of trial. The resulting cohort consisted of 42 Asian people with a mean age of 67.1 years. 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: Argentieri 2024, Kuo 2024, Han 2026, Galkin 2025, and two variants from Goeminne 2025, one trained to predict chronological age and one trained on mortality risk. The four chronological clocks (OrganAgechrono, ProtAge, ipfP3GPT, PAOPAC) correlated well with actual age (Spearman’s r ∈ 0.70–0.84) and, after ordinary least squares correction for systematic offset, achieved root mean square errors below 4 years. The two mortality-based clocks (PAC, OrganAgemortality) showed weaker correlation with chronological age (r ∈ 0.16 − 0.23), consistent with their training objective and the high disease burden of our cohort, which mortality trained models are expected to capture as elevated biological age regardless of calendar age. Bland–Altman analysis confirmed that chronological clocks carried a correctable constant bias, whereas mortality clocks exhibited a proportional bias that overestimated the age of younger participants, which could not be corrected by ordinary least squares. Interclock correlations further reflected this methodological divide: chronological clocks agreed closely with one another (r ∈ 0.78 − 0.89), whereas their correlation with mortality clocks was substantially lower (r ∈ 0.25 − 0.61. The company used six different so-called aging clocks and noticed enough of a shift on all six to conclude that the drug may modulate complementary dimensions of biological aging.
Dose-Dependent Changes and Lung Function Recovery
All six models consistently indicated a reduction in predicted biological age among patients treated with rentosertib, while those who received a placebo or no treatment saw their age increase or stay the same. The trial also tracked Forced Vital Capacity (FVC), the essential measure of lung function declining with age, which 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.
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