KMCRIC

Despite tremendous progress in our understanding of cancer biology, the majority of novel anticancer therapies fail in clinical trials, which indicates failure of conventional translational approaches. A major challenge in the development of novel anticancer agents is the design and interpretation of first-in-man trials. The key study design elements such as schedule, escalation strategy, targeted patient population, etc. rely heavily on preclinical (usually in vivo) data. This situation brings into question the predictability of preclinical tumor models as well as the method to analyze and translate the results of preclinical studies. In that regard, better in vivo models, coupled with careful translational PK/PD modeling hold the potential to significantly increase the value of preclinical results. Better predictive modeling would hopefully permit more effective evaluation of novel cancer therapeutics, (for example, cancer immunotherapeutics), as well as the more rational design of potent anticancer drug combinations. Cambridge Healthtech Institute’s Fourth Annual Predictive Pre-Clinical Models in Oncology is designed to highlight cutting edge advances in in vivo, in vitro and in silico modeling and to facilitate a discussion about effective translational approaches in cancer research.