Scientist.com has expanded its AI-powered Tumor Model Finder through a new partnership with XenoSTART, adding hundreds of high-demand, patient-derived models to the platform.

The move pushes the total number of searchable oncology models beyond 10,000 and aims to give researchers quicker, more targeted access to clinically relevant tools for preclinical cancer research.

XenoSTART is globally recognized for its deeply characterized patient-derived xenograft (PDX) models. By integrating these models into Scientist.com’s Tumor Model Finder (TMF), the collaboration aims to accelerate drug discovery timelines and improve model selection by offering translationally relevant data earlier in the research process.

“Pharma discovery teams often face critical delays accessing translational research partners with deeply characterized PDX models that accurately reflect real-world patient disease complexity,” said Michael J Wick, chief scientific officer at XenoSTART.

“By joining the Scientist.com marketplace, XenoSTART eliminates this significant barrier, providing immediate access to our highly relevant, patient-derived tumor models that are directly linked to our global clinical network. This integration empowers drug developers to confidently accelerate therapeutic decision-making and precisely guide their translational strategies.”

Scientist.com’s Tumor Model Finder is a centralized, AI-powered platform that aggregates and standardizes model data from more than 20 contract research organizations (CROs). The searchable database includes PDX, CDX, organoid, and cell line models across 17 cancer types. The platform also provides molecular data such as KRAS and BRAF mutations, drug response profiles, and RNA-seq gene expression, allowing researchers to tailor model selection to specific scientific questions.

“XenoSTART’s contribution significantly boosts the depth and quality of our TMF offerings,” said Javier Pineda, director of preclinical AI at Scientist.com. “Researchers can now find models with richer data and greater relevance to their specific cancer studies.”

Beyond convenience, the collaboration reflects a growing industry trend toward integrating preclinical data with clinical pipelines. XenoSTART’s models are directly connected to its global clinical trial network, giving researchers continuity between lab work and patient trials. The partnership is expected to enhance translational accuracy, reduce experimental uncertainty, and help guide study designs that are more predictive of clinical outcomes.