An experimental drug developed with the help of artificial intelligence (AI) has shown early signs that it could reduce markers of biological aging while treating idiopathic pulmonary fibrosis (IPF), a serious lung disease.
The drug, rentosertib, was developed by biotechnology company Insilico Medicine using artificial intelligence to identify its target and design the molecule. It was initially developed as a treatment for IPF, a progressive disease that causes scarring of the lungs and a decline in lung function.
New findings from a phase IIa clinical trial, published in Nature Biotechnology, suggest that rentosertib may also influence markers of biological age.
The researchers analyzed blood protein data from 42 patients participating in the trial and assessed their biological age using six independently developed proteomic aging clocks. All six models showed a reduction in predicted biological age among patients treated with rentosertib.
The findings have generated interest because rentosertib is not simply an existing drug being repurposed for aging. Its target, TNIK, was identified through AI-driven research into aging and disease biology, while the drug molecule itself was designed using Insilico’s generative AI platform.
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Researchers say the results point to the possibility of developing drugs that could address both age-related diseases and aspects of the aging process.
However, the findings do not mean that a drug capable of slowing or reversing human aging has been developed.
The biological age results were based on aging clocks that estimate biological age from patterns in blood proteins. These measurements are different from chronological age and do not yet establish that a person will live longer, stay healthier longer, or have their aging process permanently reversed.
The study was also small, with only 42 participants included in the aging clock analysis, and researchers say more work is needed to determine the clinical significance of the findings.
Until now, the development of Rentosertib has focused primarily on IPF. In the Phase IIa trial, published in Nature Medicine, 43 patients were evaluated with different doses of the drug and a placebo group for 12 weeks. The study reported dose-related improvements in forced vital capacity, a key measure of lung function.
The drug works by targeting TNIK, a protein that Insilico researchers identified as being involved in fibrosis and biological processes related to aging. By inhibiting TNIK, researchers hope to reduce processes that contribute to lung scarring.
Insilico has now moved rentosertib into phase III clinical development for IPF. The company announced the initiation of the late-stage trial in July 2026 after reporting encouraging Phase IIa results and manageable safety and tolerability.
The latest findings have also highlighted a broader role for AI in drug discovery.
Instead of relying entirely on conventional trial-and-error approaches to identify potential drug molecules, researchers can use artificial intelligence systems to analyze biological data, identify potential drug targets, and generate molecules designed to interact with those targets.
Insilico said its rentosertib program went from target identification to preclinical candidate nomination in about 18 months, illustrating how AI could speed up parts of the drug development process.
However, experts warn that AI remains a tool and not a substitute for clinical research. The drugs still have to go through increasingly rigorous laboratory studies and human trials to establish their safety and effectiveness.
For now, the rentosertib findings represent an early but potentially important advance in longevity research. The immediate question is whether the reduction in predicted biological age observed in a small group of IPF patients will translate into significant improvements in health and longevity in larger, longer-term studies.
With Phase III testing currently underway for its indication in lung diseases, researchers will have the opportunity to gather more evidence on the drug’s safety and effectiveness. It remains an open scientific question whether its apparent effects on biological aging can ultimately be confirmed.
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