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AI-designed drug appears to turn back the body's biological clock in early trial

The Decoderby Matthias Bastian ()3 min read1 · 1 read
AI summary · glm-5.3-flashSummaries by Vani AI

Insilico Medicine's AI-designed drug rentosertib showed reduced predicted biological age in IPF trial blood analysis, published in Nature Biotechnology.

Insilico Medicine reported that rentosertib, an AI-designed drug candidate in Phase III trials for idiopathic pulmonary fibrosis, showed reduced predicted biological age in blood protein analysis of 42 trial patients. Six independent aging-clock models built by teams at Harvard, Oxford, Beijing, and Insilico all predicted lower biological age versus placebo, with drops of three to four years by week 4 and up to six years on one clock. The drug targets TNIK and was developed with two generative AI systems in about 18 months; Eli Lilly recently invested in the company. Experts including Eric Topol and Harvard's Vadim Gladyshev called the results encouraging but not definitive, citing small sample size.

Key facts
  • Six independent aging clocks consistently predicted lower biological age versus placebo.
  • Strongest effect: 3-4 year drop by week 4, up to 6 years on one clock.
  • Drug was found and designed by AI targeting TNIK in roughly 18 months.
  • Rentosertib remains in Phase III for pulmonary fibrosis; results are correlational.
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From the original article

3 min read at the-decoder.com

A study in Nature Biotechnology suggests that rentosertib, a drug designed with AI by Insilico Medicine, may reverse markers of biological aging. Six independent aging clocks predicted that treated patients were biologically up to six years younger than the placebo group. The trial covered just 42 patients and the drug hasn't been tested in healthy people yet.

The Decoder

By Matthias Bastian · the-decoder.com

Read the full article at The Decoder

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.