Crown Bioscience and Turbine Partner on AI and Organoid Workflow in Oncology
The companies link in silico prediction with organoid validation to improve translational decision-making in drug discovery.
Crown Bioscience and Turbine announced a strategic partnership on April 21 to integrate AI-driven biological prediction with experimental validation in translational oncology, according to a company release.
The collaboration connects Turbine’s in silico Virtual Assays with Crown Bioscience’s tumor organoid assays based on HUB Organoid Technology, creating a workflow that moves from hypothesis generation to validation in a single system. The companies said this approach is designed to improve predictive accuracy while reducing experimental burden.
Turbine’s platform simulates biological responses across thousands of samples and hundreds of drugs to identify and prioritize therapeutic targets and combinations. These predictions are combined with multimodal and drug response data from Crown Bioscience’s tumor organoid models, forming a closed-loop system that links computational output with experimental testing.
Connecting Prediction With Experimental Validation
Under the partnership, researchers can use Turbine’s simulations to narrow down candidate hypotheses before validating them in Crown Bioscience’s organoid assays. The companies said this structure enables more targeted experimental design, reduces costs, and shortens development timelines.
Organoids are three-dimensional cell models derived from patient tissue that replicate aspects of human tumor biology, allowing drug responses to be tested in systems that reflect clinical conditions. Crown Bioscience’s platform includes organoid models designed to capture tumor heterogeneity and support translational research.
The integration allows predictive outputs to be tested directly in biologically relevant systems, which the companies said improves confidence in downstream decisions. The workflow is structured to generate insights earlier in the research process and focus resources on the most promising therapeutic strategies.
Addressing Scale and Translatability in Drug Discovery
The companies positioned the partnership around a constraint in drug discovery between computational scale and biological relevance. Virtual assays enable large-scale hypothesis testing, while organoid systems provide higher fidelity but are more resource-intensive.
“Translational success depends on how well early insights reflect real patient biology,” said John Gu. “By integrating predictive modeling with our organoid models, we are creating a more robust foundation for decision-making, one that improves confidence, reduces risk, and accelerates the path to the clinic.”
“Together with Crown Bioscience, we aim to address a key trade-off in drug discovery between scale and translatability,” said Szabolcs Nagy. “Using our Virtual Lab, researchers can already explore millions of hypotheses in silico. By integrating with Crown’s organoid platform, we enable virtual experimentation that better reflects patient biology, helping close the translatability gap.”
Crown Bioscience operates as a contract research organization focused on oncology and immuno-oncology, with integrated in vivo, in vitro, ex vivo, and in silico platforms. Turbine develops AI-based virtual cell models to simulate biological systems and has applied its technology across more than 30 drug discovery programs with pharmaceutical partners.
The companies said the combined system is intended to support faster and more informed decisions in preclinical development by linking large-scale computational exploration with patient-relevant experimental validation.