Predictive Oncology Develops Exclusive 3D Liver Toxicity Models for Labcorp to Accelerate Drug Discovery
核心洞察
Predictive Oncology (搜索) has successfully developed two distinct 3D liver toxicity models exclusively for Labcorp, including human and rat models that represent the liver microenvironment for drug metabolism and toxicity evaluation.
The functional organoid models enable prediction of in vivo drug clearance, drug transport, and hepatotoxicity while providing species-specific data based on physiologic hepatic microenvironments.
These models complement Predictive Oncology (搜索)'s AI-driven platform PEDAL (搜索), which predicts tumor response to drug compounds with 92% accuracy using a biobank of over 150,000 human tumor samples.
Predictive Oncology (搜索) Inc. has successfully developed two distinct 3D liver toxicity models exclusively for Labcorp as part of an ongoing agreement between the AI-driven drug discovery company and the global laboratory services leader. The models, which include both human and rat versions, represent significant advances in preclinical drug testing methodologies.
Advanced Liver Toxicity Modeling
The newly developed 3D organoid models are designed to represent the liver microenvironment and can be used for evaluating both drug metabolism and liver toxicity related to pharmaceutical compounds. According to Dr. Arlette Uihlein, Senior Vice President Translational Medicine and Drug Discovery for Predictive Oncology (搜索), "The intention of these 3D organoid models is to enable the prediction of in vivo drug clearance, drug transport and hepatotoxicity related to drugs."
The models provide highly relevant, species-specific data based on their physiologic hepatic microenvironments while capturing hepatic cellular heterogeneity. Data sets demonstrating optimal liver morphology and function were generated, including cell junction formation (ZO-1 staining), transferrin staining within hepatocytes, DCFDA staining for hepatotoxicity measurements, canalicular structure visualization, and cell viability confirmation up to 14 days.
Integration with AI-Driven Drug Discovery Platform
These 3D organoid models substantially complement Predictive Oncology (搜索)'s existing AI-driven 2D platform and 3D spheroids, which utilize human tumor samples to accelerate drug discovery and identify relevant biomarkers across thousands of patients. The platform relies on Predictive Oncology's vast biobank of over 150,000 frozen tumor samples.
The company's scientifically validated AI platform, PEDAL (搜索), is able to predict with 92% accuracy if a tumor sample will respond to a certain drug compound, allowing for more informed selection of drug/tumor type combinations for subsequent in-vitro testing.
New Approach Methodologies in Preclinical Studies
John Kendrick, Ph.D., NAMs scientific strategy lead at Labcorp, emphasized the significance of these developments: "Labcorp is committed to developing and using new approach methodologies (NAMs) in preclinical studies. These models show great promise to enable more rapid evaluation of new potential medicines and help accelerate their availability to patients."
Labcorp plans to expand this work into other species to support wider preclinical analyses and translate between animal and human models for these new alternative approaches. The functional 3D organoid models developed for Labcorp with specialized matrices and media supplements provide insights into cancer therapeutic behavior in patients.
Market Impact and Future Implications
According to Grand View Research, organoid-based platforms are key drivers in disrupting healthcare, accelerating drug development, reducing reliance on animal models, and paving the way for next-generation therapeutic solutions. Organoids are transforming disease modeling, drug discovery, and regenerative medicine, offering cost-effective and high-fidelity alternatives to traditional research methods.
The collaboration between Predictive Oncology (搜索) and Labcorp represents a significant step forward in the development of alternative testing methodologies that could reduce dependence on animal models while providing more accurate predictions of human drug responses. Together with Predictive Oncology's biobank of more than 150,000 assay-capable heterogeneous human tumor samples, the company offers academic and industry partners one of the industry's broadest AI-based drug discovery solutions.