Implications: Cancer drug testing, tissue culture, cell culture, organoids.
Unmet need: Preclinical models do not predict success in cancer clinical trials.
IP Status: University of Helsinki is drafting a patent application.
Project status: Scaling-up service, business model design.
Over 90% of oncology drugs fail due to lack of efficacy. Current preclinical models are too simplistic and poorly predictive leading to development of non-optimal therapeutics. In order to decrease the high failure rate, more precise and predictable preclinical models and technologies are required. At University of Helsinki and Aalto University, we have developed a preclinical model that is reliable, predictable and precise, allowing direct cost savings to pharma by reducing clinical trial costs and increasing success rates. The platform is based on a proprietary 3D matrix and access to patient derived tumor subtype-matched live tissue explants for certain cancer types. The matrix is suitable for human and murine cancer cell lines, primary cells and tissues, as well as 3D models such as organoids. Unlike standard commercial growth matrices, the novel model enables preservation of live patient derived tissues for weeks to months and enables drug response testing without causing the cancer cells to change their behavior radically.
Research Programs Unit, University of Helsinki