A groundbreaking study conducted by researchers at Insilico Medicine has highlighted the potential of TNIK inhibition as a novel anti-aging strategy. Utilizing an AI-driven robotics laboratory, the team identified INS018_055 (Rentosertib) – a potent small-molecule TNIK that had previously been developed by Insilico Medicine and is now in clinical trials for idiopathic pulmonary fibrosis (IPF) – as an effective senomorphic agent capable of mitigating cellular senescence. The findings were published in Aging and Disease with an impact factor of 7.843.
Generative AI has already demonstrated exceptional potential in transforming healthcare and advancing longevity research. This study exemplifies how AI can uncover dual-purpose therapeutic opportunities, addressing both disease-specific indications such as IPF and broader systemic biological aging processes. Furthermore, it underscores the powerful capabilities of our robotics lab in validating preclinical experiments with unprecedented efficiency, reproducibility, and unbiased analyses.
Qiuqiong Tang, PhD, a biologist at Insilico Medicine and the first author of the paper, explains that previous studies have shown TNIK (Traf2- and Nck-interacting kinase) plays an essential role in the cellular senescence process by orchestrating key signaling pathways closely linked to both cell senescence and fibrosis. In this recent publication, the researchers assessed Rentosertib’s potential as a senomorphic agent using a comprehensive approach that combined in vitro senescence models, multi-omics data analysis, and mechanistic evaluations.
Notably, the study was conducted exclusively in Insilico Medicine’s state-of-the-art AI-driven robotics laboratory. The lab leverages advanced AI-agent workflows across multiple stages, including sample processing and quality control, high-throughput screening, imaging, next-generation sequencing, and AI-powered analysis. These workflows not only enhance efficiency but also ensure consistent, reproducible results while minimizing biases commonly associated with manual handling.
Furthermore, the system enables a dynamic feedback loop where experimental outcomes continuously refine AI models, thereby improving precision in target discovery and indication prediction. The study demonstrated that Rentosertib significantly reduces aging-related markers such as the senescence-associated secretory phenotype (SASP) and extracellular matrix remodeling across various senescence models.
Mechanistically, the research reveals that TNIK inhibition alleviates TGF-β and Wnt signaling pathways strongly associated with senescence, fibrosis, and aging. Importantly, Rentosertib showcases safe and robust senescence attenuation while preserving healthy cell viability. This study paves the way for further exploration of Rentosertib in broader indications, particularly in idiopathic aging-related degenerative conditions.
As of the paper’s publication, Rentosertib is undergoing Phase 2 clinical trials in the U.S. and has successfully completed a Phase 2a trial in China. The results from these trials have shown promising improvements in lung function for patients with idiopathic pulmonary fibrosis (IPF). The development of Rentosertib was enabled by Insilico Medicine’s proprietary AI platform, which played a key role in identifying its therapeutic target and designing the molecule.
This process was detailed in a March 2024 Nature Biotechnology paper. That study highlighted the identification of TNIK as a novel therapeutic target for IPF and the subsequent design of Rentosertib. In 2016, Insilico first described the concept of using generative AI for designing novel molecules in a peer-reviewed journal, which laid the foundation for their commercially available Pharma.AI platform.
Since then, Insilico Medicine has continuously integrated technical breakthroughs into the Pharma.AI platform. Currently, it is an AI-powered solution spanning biology, chemistry, medicine development, and science research. Using Pharma.AI, Insilico Medicine has nominated 22 developmental/preclinical candidates (DC/PCC) in its comprehensive portfolio of over 30 assets since 2021.
The company received IND clearance for 10 molecules and completed multiple human clinical trials for two of the most advanced pipelines. Positive results were announced, highlighting their progress. By integrating AI and automation technologies, Insilico Medicine has demonstrated a significant efficiency boost compared to traditional drug discovery methods (which often require 2.5-4 years). In recent key timeline benchmarks from internal DC programs in 2021 to 2024:
- The average time to develop a candidate is between 12 and 18 months.
- Between 60 and 200 molecules are synthesized and tested per program.
- The success rate from the development stage to IND-enabling stage reaches 100%.
This research underscores the potential of combining AI with robotics in advancing pharmaceutical development and treating both specific diseases like IPF as well as broader aging-related conditions. The ongoing trials with Rentosertib are a testament to the promising impact that this approach could have on improving human health.
