Qureight, a techbio company accelerating drug development using its proprietary deep-learning image biomarkers and clinical data analytics platform, has announced the first results from a collaboration with Insilico Medicine, a clinical-stage biotechnology company driven by generative AI, focused on rentosertib (ISM001-055), a TNIK inhibitor discovered using Insilico's generative AI approach, in development for the treatment of IPF.
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Qureight’s AI-powered analytics technologies are specialised towards interstitial lung diseases such as IPF. The company’s proprietary quantitative HRCT biomarkers are predictive of disease progression and mortality in IPF patients, with the world’s largest biorepository of IPF patient data housed on the Qureight platform.
The collaboration first harnessed Qureight’s deep-learning image biomarkers to quantify that there were no statistically significant differences in baseline disease severity between cohorts in the rentosertib Phase IIa trial, further supporting the study’s positive preliminary outcome. Clinical data held in the Qureight platform was then used to demonstrate that the enrolled IPF patient population was fully representative of global IPF populations. These results suggest the positive results seen in the Phase IIa study will be replicated in larger IPF populations across other patient cohorts, and support Insilico’s planned expansion of rentosertib’s clinical trials.
Muhunthan Thillai, MD, co-founder and CEO of Qureight, said: “We’re delighted that Insilico has partnered us at this stage in the clinical development of rentosertib. The results from this initial project demonstrate the impact of our expertise, AI-powered analytics platform, and specialised patient datasets, to support the progression of promising new therapies. We hope to extend the application of our technologies to future development stages to accurately quantify the impact of TNIK as a novel mechanism for the treatment of IPF patients, and further evidence future outcomes.”
Rentosertib is based on a novel TNIK (TRAF2 and NCK interacting kinase) inhibitor, developed using Insilico's generative AI approach. In IPF, the activation of TNIK drives pathological fibrosis in the lungs, contributing to the progressive decline in lung function. By inhibiting TNIK, rentosertib aims to halt or reverse fibrotic processes, offering a disease-modifying treatment for patients with IPF. Insilico recently reported encouraging results from the Phase IIa study of rentosertib in this cohort of IPF patients in Nature Medicine1, demonstrating preliminary clinical efficacy as measured by improvement in forced vital capacity (FVC) at 12 weeks. The study clinically validated TNIK – a novel target identified through the use of generative AI – for the first time as a biological mechanism of treating IPF.
Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine, said: “Rentosertib has demonstrated promising potential to provide meaningful clinical benefits for IPF patients globally. Through our work with Qureight we have also been able to show, for the first time, that our Phase 2a IPF patients are comparable in disease state with other global cohorts. Our collaboration with Qureight illustrates the transformative potential of AI in both drug discovery and development, paving the way for faster and more innovative therapeutic and clinical trial advancements.”