Conventional approaches to predicting drug-induced liver injury (DILI) often fall short—lacking the physiological relevance, scalability, and accuracy needed to support today’s fast-moving research and development pipelines. Many existing models are either too simplistic to capture complex liver responses or too costly and variable to deploy at scale. 
 

LifeNet Health and Axiom have partnered to redefine how scientists approach drug safety. By combining the physiological relevance of TruVivo’s primary human liver cell platform with Axiom’s advanced AI-driven analytics, this collaboration delivers a next-generation tool for predicting drug-induced liver injury (DILI). TruVivo captures detailed phenotypic responses using high-content imaging—including cell painting techniques—while Axiom’s machine learning models connect these profiles to real-world clinical outcomes. The result: a scalable, interpretable, and cost-effective DILI risk assessment solution that empowers scientists to make safer, faster decisions in early drug development.
 

Key Takeaways:
•    Harnessing TruVivo + Axiom AI to generate precise, interpretable clinical risk predictions using real-world biological and clinical data.
•    Best practices for model validation that meet and exceed current industry standards for toxicity prediction.
•    Embedding AI/ML in preclinical workflows with TruVivo to maximize scientific insight while reducing time, cost, and resource strain.
•    Enabling advanced cell painting imaging on the TruVivo system to uncover subtle cellular phenotypes for mechanism-of-action and toxicity profiling.
•    Delivering affordable, scalable preclinical models through the Axiom and LifeNet Health partnership—democratizing access to high-performance risk assessment tools.