Leveraging New and Established Preclinical Oncology Models for Translational Success

The success rate of anti-cancer therapeutics has historically been low, hovering around 5%, and may attributed to the widespread use of imperfect models in the preclinical phase. We will discuss the advantages and disadvantages of the latest translational models and tools enabling therapeutic development in oncology. A special emphasis will be given to the importance of context in enhancing the predictive potential of preclinical models. Will we discuss context in terms of choosing appropriate tumor models (cell lines, PDX, reporters, and biomarkers), tissue contexts (ectopic, orthotopic, heterotopic and metastatic models), treatment contexts (the recapitulation of clinical treatment paradigms), stromal contexts (microenvironment, infiltrating cells, extracellular matrix etc.), host contexts (especially immunological contexts and considerations in performing immunoncology studies), and influences of the microbiome (particularly in immunoncology studies and an overview of Biomodels’ capabilities in running oncology models under germ-free or gnotobiotic conditions). With thoughtful design and innovative application, Biomodels’ currently available preclinical models can be rationally leveraged to provide novel cancer therapies the best chance of success in the clinical phase.