Quris-AI Acquires Nortis to Enhance Drug Safety with Kidney-on-Chip Tech

Quris-AI, a company dedicated to enhancing drug safety prediction, has announced its acquisition of Kidney-on-Chip pioneer Nortis, also known as Numa Biosciences, Inc.

The addition of Nortis’s microphysiology systems (MPS) technology to Quris-AI’s platform is set to improve the precision of renal toxicity assessments and pharmacokinetic predictions in both preclinical and clinical drug development phases.

Nortis’s Kidney-on-Chip technology, recognized by the National Institutes of Health’s National Center for Advancing Translational Sciences (NCATS) and utilized in projects such as the “Tissue Chips in Space” initiative to study kidney function in microgravity, is widely regarded for advancing in-vitro testing standards for precision in drug development. Integrated with Quris-AI’s advanced machine learning models and its proprietary patient-on-chip system, Nortis’s models will support earlier identification of harmful drug candidates, refining the overall accuracy and efficiency of drug safety predictions.

Dr. Isaac Bentwich, Founder and CEO of Quris-AI:

“We are thrilled to incorporate Nortis's pioneering technology into the Quris-AI platform. This acquisition represents a powerful synergy between Nortis’s legacy of scientific excellence and Quris’s cutting-edge Bio-AI capabilities to significantly enhance the accuracy of drug safety predictions in both pre-clinical and clinical phases. We are dedicated to continuing to push the boundaries of drug safety prediction and personalized medicine, ultimately leading to better patient outcomes and advancing the future of healthcare.”

Beyond the integration, Quris will continue Nortis's collaborations with regulatory and research bodies, including the FDA, to further research on kidney diseases, broadening its impact on clinical research and drug safety.

See also: How Industry Embraces Organ-on-Chips: A 2024 Status Report

With 30 granted and pending patents, Quris-AI’s dual-headquartered operations in Boston and Israel focus on machine learning-powered predictions aimed at reducing clinical trial failure rates. Its platform, leveraging 3D physiologically relevant organ models and genetically diverse stem-cell-derived tissue data, aims to provide a refined predictive approach to model human responses, with the goal of enhancing accuracy and efficiency in the drug development process.

For more details, visit Quris-AI’s website.

Topics: Tools & Methods