A groundbreaking study conducted by researchers at Insilico Medicine has shed light on the potential of TNIK inhibition as an innovative anti-aging strategy. Utilizing an AI-driven robotics laboratory, the team identified INS018_055 (Rentosertib) – a potent small-molecule TNIK that was previously developed by Insilico Medicine and is already in clinical trials for idiopathic pulmonary fibrosis (IPF). The study reveals Rentosertib as an effective senomorphic agent capable of mitigating cellular senescence. These findings were published in Aging and Disease, which has a high impact factor of 7.843.
Generative AI is already making significant strides in transforming healthcare and advancing longevity research. This study exemplifies how AI can uncover dual-purpose therapeutic opportunities that address both disease-specific conditions like IPF and broader systemic biological aging processes. Additionally, it highlights the powerful capabilities of our robotics lab in validating preclinical experiments with unparalleled efficiency, reproducibility, and unbiased analyses.
Qiuqiong Tang, PhD, a biologist at Insilico Medicine and the first author of this paper explains that previous studies have shown TNIK (Traf2- and Nck-interacting kinase) plays a crucial role in cellular senescence by orchestrating key signaling pathways associated with both cell senescence and fibrosis. In their recent publication, researchers assessed Rentosertib’s potential as a senomorphic agent using a comprehensive approach that combines in vitro senescence models, multi-omics data analysis, and mechanistic evaluations.
The study was conducted exclusively within Insilico Medicine’s state-of-the-art AI-driven robotics laboratory. It leveraged advanced AI-agent workflows across multiple stages of research, including sample processing and quality control, high-throughput screening, imaging techniques such as next-generation sequencing, and AI-powered analysis. This integrated approach not only boosts efficiency but also ensures consistent results with minimal bias associated with manual handling.
The study found that Rentosertib significantly reduces aging-related markers like the senescence-associated secretory phenotype (SASP) and extracellular matrix remodeling in various senescence models. Mechanistically, it was revealed that TNIK inhibition alleviates key pathways involved in senescence, fibrosis, and aging such as TGF-β and Wnt signaling.
Importantly, Rentosertib demonstrated safe attenuation of cellular senescence while preserving the viability of healthy cells. These findings pave the way for further investigation into its potential use in a broader range of indications, particularly in age-related degenerative conditions.
Rentosertib is currently undergoing Phase 2 clinical trials in the U.S., and it has completed successful Phase 2a trials in China, showing promising results in improving lung function for IPF patients. The development of Rentosertib was enabled by Insilico Medicine’s proprietary AI platform that played a pivotal role in identifying its therapeutic target and designing the molecule.
These advancements are detailed in a March 2024 paper published in Nature Biotechnology, which highlighted TNIK as a novel therapeutic target for IPF and described the subsequent design of Rentosertib. Since Insilico Medicine first explored using generative AI to design new molecules in 2016 – this work laid the groundwork for its commercially available Pharma.AI platform.
The company continues to integrate cutting-edge technological breakthroughs into Pharma.AI, a platform that spans biology, chemistry, medicine development and scientific research. By utilizing these technologies, Insilico Medicine has nominated over 30 assets since 2021, including 22 developmental/preclinical candidates (DC/PCC), received IND clearance for ten molecules, and completed several human clinical trials with positive outcomes.
Insilico’s integrated approach of AI and automation significantly enhances efficiency in drug discovery. Compared to traditional methods that can take between 2.5-4 years, Insilco Medicine achieves an average time-to-drug candidate (DC) period of 12-18 months. They synthesize and test approximately 60-200 molecules per program with a success rate from the DC stage to IND-enabling stage reaching 100%.
