OBJECTIVE: Intravesical adoptive cell therapy with tumor-infiltrating lymphocytes (ACT-TIL) increases the number of tumor-specific T cells at bladder tumor site. However, the efficacy of these T cells is limited due to immune suppression from myeloid-derived suppressor cells (MDSCs). Treatment with TIL in combination with gemcitabine, which depletes MDSCs at the tumor site, has shown efficacy in a preclinical model [1]. Our aim is to develop a virtual cohort from this data for future mathematical optimization of the treatment regimen that will enable a reduction in the number of mice experiments. METHODS: Ultrasound and histology data were collected from mice with orthotopic MB49 bladder tumors treated with OT-1 cells and gemcitabine. We developed an ODE model consisting of cancer, T cells, and MDSCs with the two treatments. Structural identifiability using the differential algebra approach identified a suitable data type for model fitting. Practical identifiability was performed to determine parameters to vary for virtual cohort sampling. Parameter distributions representative of data were produced using a data-informed error threshold for the Approximate Bayesian Computation (ABC) rejection method. RESULTS: From structural identifiability analysis, model parameters are not identifiable with respect to total tumor volume data. Instead, we found that data from each cell type (cancer cells, T cells, and MDSCs) is appropriate for model fitting. Using histology and flow cytometry data, we interpolated percentages of the three cell types in the total tumor over time and used them to modify the ultrasound data to produce volume data on each cell type. This data was used to fit the model. Practical identifiability analysis showed that the tumor growth rate (p_C) and the homeostatic native T cell population (T_0) could be identified using ultrasound data alone. Thus, these parameters were not only used to generate the virtual cohort with virtual mice selected using the ABC method, but p_C and T_0 can also be used for the future creation of digital twins using ultrasound data during experimental validation of an optimized regimen. CONCLUSION: This virtual cohort framework can be applied to other diseases beyond cancer. In the future, such cohorts can be used to determine a robust, optimized treatment regimen, suitable for most subjects, and then validated in a preclinical model. CITATIONS: [1] Bazargan et al. (2023). Frontiers in Immunology, 14, 1275375. https://doi.org/10.3389/fimmu.2023.1275375