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AI-designed medicine shows early promise for pulmonary fibrosis patients in clinical trial
A groundbreaking Phase 2A study shows that RentoSertib, an AI-engineered TNIK inhibitor, may offer new hope for idiopathic pulmonary fibrosis patients after demonstrating improved lung function and a favorable safety profile. In a recent study in the journal Natural Medicine, researchers report the results of the clinical trial of RentoSertib (formerly ISM001-055). RentoStib is an AI-discovered TNIK inhibitor that shows promise in improving lung function as measured by forced vital capacity in patients with idiopathic pulmonary fibrosis (IPF). While RentoSertib is still in clinical development and represents a significant step forward as one of the few AI-discovered drugs to reach this stage of human trials,...
AI-designed medicine shows early promise for pulmonary fibrosis patients in clinical trial
A groundbreaking Phase 2A study shows that RentoSertib, an AI-engineered TNIK inhibitor, may offer new hope for idiopathic pulmonary fibrosis patients after demonstrating improved lung function and a favorable safety profile.
In a recent study published in the journalNatural medicineResearchers report clinical trial results of RentoSertib (formerly ISM001-055). RentoStib is an AI-discovered TNIK inhibitor that shows promise in improving lung function as measured by forced vital capacity in patients with idiopathic pulmonary fibrosis (IPF). While still in clinical development, RentoSertib represents a significant advance as one of the few AI-discovered drugs to reach this stage of human trials and offers hope for a novel therapeutic approach for IPF. It is important to note that these early phase results, although encouraging, do not guarantee future clinical benefit or regulatory approval.
The 12-week study evaluated the safety and effectiveness of multiple dosage combinations of RentoSertib or an equivalent placebo. Study results from 71 participants found that overall treatment event rates (TEAEs) were generally similar across study cohorts. However, treatment-emergent AEs were more common in the RentoSertib groups compared to placebo, although serious treatment AEs were low and comparable. The once-daily dose of 60 mg also demonstrated potential efficacy, increasing participants' forced vital capacity by +98.4 mL and supporting the inclusion of RentoSertib in larger, longer-term studies.
background
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, age-related lung disease. It is characterized by the irreversible scarring and thickening of lung tissue, leading to death from respiratory failure.
While rare (10-60 cases per 100,000 Americans), the disease has no cure and inevitably leads to patient death (median survival = 2-4 years). Current pharmacological interventions (nintedanib and pirfenidone) aim to delay disease progression and highlight the need to discover more effective treatments. Unfortunately, conventional drug discovery is slow (10-15 years), laborious, and extremely expensive ($2 billion to $3 billion).
Recent advances in artificial intelligence (AI) generative algorithms and discovery research have demonstrated that these tools can identify and design novel pharmaceuticals at a fraction of the time and economic cost of traditional approaches.
Previous investigations by the current study group used generative AI to not only identify an IPF-associated drug target (TRAF2 and NCK interacting kinase [TNIK]) but also design an inhibitor molecule ('RentoSertib', formerly ISM001-055) with the potential to target Tnik's profibrotic and to counteract proinflammatory effects. RentoSertib has already cleared Phase 0 and 1 clinical trials, demonstrating safety and high tolerance in healthy people.
About the study
The present study reports the results of RentoSertib Phase 2A clinical trials. The study used a randomized, placebo-controlled, multi-dose study design to evaluate the safety and efficacy of RentoSertib in a cohort of Chinese IPF patients between July 19, 2023 and June 11, 2024. The study recruited 128 IPF patients (age >40 years), 57 were excluded due to comorbidities or recent respiratory infections.
The remaining 71 patients were randomly assigned to one of four intervention cohorts: 30 mg of drug once daily (qd, n = 18), 30 mg of drug twice daily (bid, n = 18), 60 mg of drug qd (n = 18), or placebo (n = 17). The study lasted 12 weeks and included frequent assessments. The primary objective was to assess safety and tolerability, with the primary endpoint being the percentage of patients with at least one course of treatment (TEAE). Additional assessments included forced forcing capacity (FVC) metrics, questionnaire-assessed cough-associated quality of life (QoL), and blood samples collected at baseline (week 0), weeks 2, 4, 8, and 12 for pharmacokinetic (PK) authatching and biomarker analysis.
Endpoints were assessed using analysis of covariance (ANCOVA), with treatment (drug or placebo) coded as a fixed effect and participant baseline values coded as covariates. PK parameters were determined from plasma concentrations while proteomic profiling of serum samples was performed to understand the mechanism of action and identify potential biomarkers of response.
Study results
Of the 71 participants included in the clinical trial, 16 (placebo = 2, 30 mg RentoStib qd = 2, 30 mg RentoStib bid = 6, 60 mg RentoStib qd = 6) received treatment before completion of the study due to adverse events (AEs) or non-inflammatory exceptions. The most common AEs leading to cancellation were related to liver injury or dysfunction (7 of 12 AE-related discontinuities), with diarrhea also being a factor. Of note, participants who discontinued were included in drug safety assessments.
Primary endpoint analyzes showed that overall treatment care (TEA) rates were similar across cohorts, with 70.6% of placebo, 72.2% of 30 mg QD, and 83.3% of 30 mg BID and 60 mg QD groups reporting at least one TEA. However, treatment-emergent AEs were more common in patients receiving RentoSertib (between 50.0% and 77.8%) compared to placebo (29.4%). Serious treatment AEs were rare and comparable across treatment groups (0% in placebo, 5.6% in 30 mg QD, 11.1% in 30 mg BID, and 11.1% in 60 mg QD). The most common teas were hypokalemia, abnormal liver function, diarrhea and increased alanine aminotransferase (ALT). Four of the seven participants who withdrew due to liver toxicity received concurrent antifibrotic therapy of nintedanib.
Efficacy analyzes demonstrated the potential therapeutic benefit of higher doses of RentoStib. FVC assessments showed a mean change from baseline of -20.3 mL in the placebo group, -27.0 mL in the 30 mg QD group, +19.7 mL in the 30 mg command group, and +98.4 mL in the 60 mg QD group. Notably, patients who did not take SOC drugs and 60 mg RentoSertib QD concomitantly showed FVC increase of +187.8 mL. Conversely, patients who took 60 mg RentoSertib QD concomitantly with nintedanib or pirfenidone showed no significant changes in FVC.
Regarding other lung function metrics, changes in DLCO and FEV1 were relatively small and similar across treatment groups. The impact on overall quality of life (QOL) was largely inconclusive, although modeling indicated a significant improvement in Leicester Cough Questionnaire (LCQ) scores for patients receiving 60 mg RentoStib QD.
Importantly, three patients (16.7%) in the 60 mg QD group experienced an acute exacerbation of IPF (AE-IPF) compared to one patient (5.9%) in the placebo group (not hospitalized). The paper notes that a 12-week study is a short time frame to capture such long-term events.
Conclusions
The present Phase 2A clinical trial shows that RentoSertib was generally safe and well tolerated over 12 weeks, with overall withdrawal rates similar to placebo, although treatment AEs were more common with RentoSertib. Serious AEs related to treatments were rare. The study also showed promising efficacy signals, notably an FVC increase of +98.4 ml in the 60 mg QD group (and up to a +187.8 ml FVC increase in those not on SoC antifibrotics in this dosage group). These results suggest the potential of the drug as a novel therapeutic agent for IPF and warrant further research in larger, longer-term studies.
The study authors acknowledge limitations, including the small cohort size per arm, the geographic and demographic homogeneity of participants (all Chinese residents of similar race), and the short follow-up period, which limit the assessment of long-term safety and effectiveness.
Notably, this AI-discovered TNIK inhibitor highlights the potential of generative AI-driven drug discovery in pharmacological research and development, suggesting that AI can significantly improve the efficiency of new drug discovery.
One post made my day today. Dr. @Erictopol Read our big paper. In 2019, most people were skeptical about the potential of generative AI in Medchem. Today, both target ID and chemistry can be performed quickly and on autopilot. Validation time and cost represent the biggest challenge
– Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs) (@biogerontology) June 3, 2025
The first generative AI drug to reach a phase 2 randomized clinical trial. The small molecule in pill form, RentoSertib, showed signs of effectiveness for idiopathic pulmonary fibrosis @Naturemedicine @Insilicomeds pic.twitter.com/nmbtvnssim
– Eric Topol (@erictopol) June 3, 2025
Posted in: Drug Trial News | Medical Research News | Illness News | Pharmaceutical News
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Written by
Hugo Francisco de Souza
Hugo Francisco de Souza is an academic writer based in Bangalore, Karnataka, India. His academic passions lie in biogeography, evolutionary biology and herpetology. He is currently pursuing his doctorate. From the Center for Ecological Sciences, Indian Institute of Science, where he studies the origins, distribution and speciation of wetlands. Hugo has received, among other things, the DST-Inspire scholarship for his doctoral thesis and the gold medal from Pondicherry University for academic excellence during his masters. His research has been published in high-quality journals including PLOs, Neglect Tropical Diseases and Systematic Biology. When Hugo isn't working or writing, he can be found consuming plenty of anime and manga, composing and making music with his bass guitar, shredding trails on his MTB, playing video games (he prefers the term "gaming"), or tinkering with all things tech.
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Sources:
- Xu, Z., Ren, F., Wang, P. et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat Med (2025), DOI = 10.1038/s41591-025-03743-2, https://www.nature.com/articles/s41591-025-03743-2