Peter Eide began his presentation by highlighting the lack of accuracy in both clinical cancer care and drug development, suggesting a connection between the two. Eide noted the unique nature of cancers, each presenting significant functional diversity. He criticised the use of simplified model systems, such as inbred cell lines and mice, which hindered progress.
Eide referenced the complexity of predicting outcomes in cancer research, drawing an analogy to the "Three Body Problem" in physics. He proposed that instead of predicting, researchers should focus on measuring. He described the use of more complex model systems, including 3D structures of cancer cells that mimic the microarchitecture of the original tumour. These advanced models, he argued, were more precise, predictive, scalable, and cost-effective for cancer drug development.
Eide explained that his company aimed to integrate these models into clinical use, providing clinicians with reports on suitable drugs for each cancer patient. He acknowledged the long pathway for a startup but emphasised the potential for these models in drug development. He mentioned collaborations with major pharmaceutical companies like Merck and Roche, as well as smaller biotechs in Sweden.
Eide highlighted the success of Isofol, which raised €10 million based on data generated from their project. He discussed ongoing clinical trials, including one starting in August with 75 patients, to document the clinical utility of their technology. He also mentioned completed and ongoing studies in pancreatic and colorectal cancer.
Eide presented a heat map showing the heterogeneity in drug response among patients. He discussed the potential of their technology to identify novel drug sensitivities and improve patient outcomes. He concluded by summarising the benefits of patient-derived tumoroids, their ability to generate clinically relevant data, and their potential to enhance drug development.
