From algorithm to late-stage clinic: a milestone for AI-driven pharma
Insilico Medicine has pushed its lead asset into Phase III human trials. The drug, rentosertib, targets idiopathic pulmonary fibrosis (IPF) — a devastating lung disease with a median survival of just two to four years after diagnosis. This is no small step. It marks one of the few times an AI-discovered molecule has moved beyond early safety checks into large-scale efficacy testing.
The company’s proprietary platform, Pharma.AI, did the heavy lifting. It identified the biological target, designed the molecule, and predicted how it would behave in the body. Now, regulators and patients will see if those predictions hold up at scale.
What is IPF and why rentosertib matters
IPF scars lung tissue relentlessly. Patients lose the ability to breathe properly. Existing drugs slow progression but don’t stop it — and they come with side effects. Rentosertib works differently. It inhibits an enzyme called TNIK (TRAF2- and NCK-interacting kinase), which sits at the crossroads of several pathways driving fibrosis, inflammation, and aging-related tissue damage.
A Phase IIa trial, conducted across 22 sites in China, tested 71 patients. One group got a placebo. Another received 30 mg or 60 mg daily doses of rentosertib for 12 weeks. The results were striking: patients on the 60 mg dose gained an average of 98.4 mL in forced vital capacity (a key measure of lung function), while the placebo group lost 20.3 mL. The U.S. Food and Drug Administration (FDA) had already granted orphan drug designation in February 2023.
How PandaOmics found the target no one else saw
The discovery pipeline started with PandaOmics, one of three engines inside Pharma.AI. This system chews through genomics, clinical data, scientific literature, and patent filings. It builds biological network models and applies causal inference — a statistical method that goes beyond correlation to identify likely drivers of disease.
PandaOmics flagged TNIK as the prime target for IPF intervention. That was unconventional. Most IPF drugs target receptor tyrosine kinases. TNIK sits deeper in the signaling web, regulating pathways like Wnt, TGF-β, Hippo/YAP-TAZ, JNK, and NF-κB. The algorithm also scored TNIK high on a hallmarks-of-aging framework, linking it to chronic inflammation, fibrosis, and cellular senescence.
Feng Ren, PhD, Co-CEO and Chief Scientific Officer of Insilico Medicine, put it plainly: “Rentosertib was not discovered by starting from a conventional target and simply screening more compounds. It came from a biology-first, ageing-informed AI workflow.”
Chemistry42: generative design, not library screening
Once the target was locked, the Chemistry42 engine took over. This is not your typical high-throughput screening, where robots test millions of compounds from a library. Chemistry42 builds molecules from scratch using Generative Tensorial Reinforcement Learning. It designs structures that fit the target protein’s pocket while balancing drug-like properties — solubility, metabolism, toxicity.
The system generated just 79 physical molecules for testing. The team selected the 55th iteration for preclinical development. From project start to preclinical candidate nomination: 18 months. That’s fast by industry standards. The underlying methodology, called GENTRL, was published in Nature Biotechnology back in 2019.
Proteomic clocks and senescence markers in the clinic
Insilico didn’t stop at standard clinical endpoints. The Phase IIa trial included exploratory proteomic analysis using aging-clock frameworks. Tools like ProtAge, OrganAgechrono, and ipfP3GPT tracked predicted biological age changes. The team compared treatment-responsive proteins against UK Biobank trajectories.
Mortality-risk clocks (PAC and OrganAgemortality) provided additional layers of analysis. SenMayo and CellAge signatures measured senescence and senescence-associated secretory phenotype activity. Peer-reviewed work in Aging and Disease confirmed that TNIK inhibition produces senomorphic effects — reducing extracellular matrix remodeling markers.
Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine, framed the bigger picture: “This program began with the hypothesis that ageing biology could help identify powerful targets for major diseases. It has now advanced through target discovery, molecular design, preclinical validation, Phase I safety, randomised Phase IIa clinical data, and into Phase III development. For the AI drug discovery field, this is no longer only a speed story — it is a clinical translation story.”
What Phase III will test — and why it matters for AI in biopharma
The upcoming Phase III trial will enroll hundreds of patients across multiple countries. It will measure lung function, progression-free survival, and safety over a longer period. Success would validate not just rentosertib, but the entire AI-driven approach to drug discovery.
Several publications document the journey so far. Nature Biotechnology covered the discovery-to-clinic arc. The Journal of Medicinal Chemistry published the structural biology — including the TNIK kinase domain co-crystal structure. Nature Medicine reported the Phase IIa data.
For the broader industry, this is a test case. Can AI really originate new biology and new chemistry, not just accelerate old methods? The answer will come from patients, regulators, and the data. Phase III is where that answer starts taking shape.
Want to learn more about AI in drug discovery? Check out our coverage of NVIDIA BioNeMo’s role in accelerating research and how generative AI is reshaping clinical trials.
