Evolutionary therapy is inherently a dynamic and proactive approach to cancer treatment. This necessitates the development and use of many components typically not used in standard of care: 1) mathematical modeling is used to capture longitudinal dynamics via mechanism; 2) clinical and pre-clinical data is needed to calibrate such models and capture heterogeneity across a population; 3) an analytical framework for implementing digital twins and/or virtual patients is needed; and 4) the methods must be feasibly translatable to the clinic (for individual patient treatment decision support) and clinical trial developers (for trial design and execution). Based upon the experience of developing these components for several clinical trials (including two ‘Evolutionary Tumor Board’ (ETB) trials, NCT04343365 and NCT06423950), we summarize results and insights gained in this space. For individual patients, over 30 patients have enrolled in the ETBs for guided decision support. In addition to offering personalized evolutionary therapy options, this research has a secondary benefit of generating new hypotheses regarding how to interpret clinical data and exploit it for clinical use. Fundamental to the ETBs is the development of digital twins calibrated from retrospective data. For trial design and execution, an integrated virtual patient framework (IVPF) is being used to predict trial outcomes as well as find optimal trial design strategies. Furthermore, the IVPF can be used as a parallel virtual trial to a real trial, leveraging deep clinical calibration to provide early outcome predictions and real time mechanistic analysis of incoming trial data.