Merck KGaA Adopts Quris-AI’s Organ-On-Chip Platform for Preclinical Drug Safety Testing
Merck KGaA has integrated Quris-AI’s Bio-AI platform into its drug development pipeline to evaluate small molecule drug candidates for safety before clinical trials. The collaboration builds on a two-year validation study, where Quris-AI demonstrated higher accuracy in predicting drug-induced liver injury (DILI) compared to traditional preclinical methods, with the potential to reduce reliance on animal testing.
See also: Beyond Animal Testing: Organ-on-a-Chip Companies Usher in a New Era for Drug Trials
According to Prof. Robert S. Langer, a Moderna Co-Founder and Former Chairman of the FDA Science Board, currently member of the Quris-AI Scientific Advisory Board, the platform is "uniquely positioned to address the FDA Modernization Acts 2.0 and now 3.0, which push to use AI, organ-on-chip, and other advanced technologies to replace antiquated animal testing."
This partnership follows an earlier expansion in September 2023, when Merck KGaA extended its collaboration with Quris after a successful preclinical study that identified liver toxicity risks with high accuracy.
In November 2024, Quris-AI acquired Nortis, a company known for its Kidney-on-Chip technology. Nortis’s platform has been used in the NIH’s “Tissue Chips in Space” program, which investigates kidney function in microgravity. By incorporating Quris-AI’s platform, the system now benefits from enhanced AI-driven safety predictions for kidney toxicity. The technology uses human-derived kidney tissue to simulate complex organ functions, such as filtration and electrolyte balance, offering insights beyond the reach of traditional animal models. So far, research has already shown its ability to detect nephrotoxic effects that animal testing missed.
Quris-AI’s platform combines machine learning, generative AI, and patient-on-chip systems to model human responses to drug candidates. The system integrates high-throughput 3D organ models, real-time monitoring of biomarkers, and genomic diversity derived from stem cells. By simulating interactions across multiple interconnected organ models, the platform generates datasets that inform predictive algorithms. These algorithms are continuously refined through new experimental data, enabling more accurate assessments of drug safety and efficacy while reducing reliance on animal testing.
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