For years, the promise of artificial intelligence in drug discovery has lived mostly in press releases and conference decks. Researchers have talked about what AI could do — compress timelines, identify overlooked targets, and design better molecules. What we haven’t seen much of, until now, is an AI-designed drug actually walking through clinical trials with a proper name and a shot at reaching patients. That changed earlier this month when the United States Adopted Names (USAN) Council granted an official generic name, Rentosertib, to a drug candidate for idiopathic pulmonary fibrosis (IPF) developed by Insilico Medicine, a biotechnology company headquartered in Hong Kong with research operations spanning the US, China, and the UAE.
The naming is more than administrative housekeeping. In pharmaceutical development, receiving a USAN name signals that a compound has cleared early hurdles and is being taken seriously as a future medicine.
Why this Drug is Different
Most AI-assisted drug programs use machine learning at one point in the pipeline, maybe to screen compound libraries faster, or to predict toxicity. Rentosertib is unusual because AI didn’t just assist; it drove the entire discovery from the ground up.
Insilico’s platform, called Pharma.AI, tackled the problem in two stages. First, an engine called PandaOmics combed through large biological datasets to identify a target worth pursuing for idiopathic pulmonary fibrosis (IPF), a brutal lung disease that scars tissue progressively and carries a median survival of just three to four years. The target it landed on was TNIK, a kinase not previously well-explored in the context of IPF. Then, a separate engine, Chemistry42, designed small molecules to hit that target, ultimately nominating Rentosertib as the lead candidate. The whole process, from target identification to preclinical nomination, took 18 months — a fraction of the conventional timeline.
What the Clinical Data Actually Shows
Early safety trials in New Zealand and China, run in healthy volunteers, showed the drug was well-tolerated with a clean pharmacokinetic profile. That opened the door to a Phase IIa trial in actual IPF patients.
The results were genuinely encouraging. Over 12 weeks, patients on the highest dose, 60mg once daily, showed a mean improvement of 98.4 mL in forced vital capacity, the standard measure of how much air lungs can push out. The placebo group, by contrast, declined by 62.3 mL on average. That’s a meaningful swing in a disease where functional decline is the norm. Patients also reported reductions in cough and improved respiratory symptoms, which matters enormously for quality of life even before efficacy endpoints come into play.
The Human Still in the Loop
There’s a detail in the naming itself worth pausing on. “Rentosertib” partially honors Dr. Feng Ren, Insilico’s Co-CEO and Chief Scientific Officer. Alex Zhavoronkov, the company’s founder, noted that the name reflects the interplay between human expertise and artificial intelligence — not AI replacing scientists, but the two working in tandem.
That framing is important. The story of Rentosertib isn’t really about a machine inventing medicine autonomously. It’s about what happens when experienced researchers ask better questions, and AI helps answer them faster.
Insilico is now in conversations with global regulatory agencies to plan larger pivotal trials. If those succeed, Rentosertib would become the first AI-discovered therapy to receive regulatory approval, a milestone that would reshape how the pharmaceutical industry thinks about the earliest stages of drug development.
The drug still has a long road ahead. But it has a name now, and in this field, that counts for something.
Article Source: Reference Article
Disclaimer:
The research discussed in this article was conducted and published by Insilico Medicine. CBIRT has no involvement in the research itself. This article is intended solely to raise awareness about recent developments and does not claim authorship or endorsement of the research.
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Anchal is a consulting scientific writing intern at CBIRT with a passion for bioinformatics and its miracles. She is pursuing an MTech in Bioinformatics from Delhi Technological University, Delhi. Through engaging prose, she invites readers to explore the captivating world of bioinformatics, showcasing its groundbreaking contributions to understanding the mysteries of life. Besides science, she enjoys reading and painting.
