Establishing effective therapeutic schedules for cancer immunotherapies with computational biology

Oncolytic virotherapy is a promising strategy in cancer immunotherapy. Unfortunately, the pace of oncolytic virus approval is slow and clinical trials have been met with failures. Comprehensive computational and mathematical models can alleviate attrition along the drug development pipeline by helping to assess combination therapeutic strategies, stratifying responders and non-responders, and providing insight into the biological mechanisms driving treatment response. Here I will discuss our recent work focusing on the preclinical-to-clinical translational of oncolytic viruses using computational biology.

Email ciocanel@math.duke.edu to request the Zoom link and password for the talk (or subscribe to announcements at https://lists.duke.edu/sympa/info/mathbio-seminar).