Multiscale Models Guided By Cellular Dynamics Quantification for Predicting Optimum Immune and Targeted Therapy Schedules
The principal goal of this proposal is to combine multiscale mathematical modeling with novel model-driven experimental platforms in order to gain a deeper, more robust understanding of tumor-immune dynamics. Using bladder cancer as our platform, we are developing a comprehensive and predictive 3D computational framework that will be used to optimize combination immunotherapy and receptor kinase targeted therapy. Our experimentally-driven multiscale approach is poised to significantly enhance the current understanding of differential cell-kill mechanisms on tumor-immune outcomes and to improve the ability to combine promising drugs for clinical trials.
MODELING FGFR-3 TARGETED THERAPY IN COMBINATION WITH IMMUNE CHECKPOINT INHIBITORS
We developed a preliminary mathematical model for FGFR3-mediated tumor growth and used it to investigate the impact of the combined administration of a small molecule inhibitor of FGFR3 and a monoclonal antibody against the PD-1/PD-L1 immune checkpoint. The model is carefully calibrated and validated with experimental data before survival benefits, and dosing schedules are explored. Predictions of the model suggest that FGFR3 mutation reduces the effectiveness of anti-PD-L1 therapy, that there are regions of parameter space where each monotherapy can outperform the other, and that pretreatment with anti-PD-L1 therapy always results in greater tumor reduction even when anti-FGFR3 therapy is the more effective single therapy. https://onlinelibrary.wiley.com/doi/10.1002/cso2.1019
A NEW METHOD FOR SIMULATING MULTISCALE AGENT-BASED MODELS
We developed a novel yet intuitive approach that reduces the time complexity for simulating multiscale agent-based models (ABMs) that results in a speedup of 1-2 orders of magnitude. Our method allows for more thorough explorations of ABMs with larger numbers of agents than previously achievable.
A NEW METHOD FOR BRIDGING THE GAP BETWEEN AGENT-BASED MODELS AND EXPERIMENTAL DATA
We developed a first-of-its-kind method that leverages explicitly formulated surrogate models to bridge the current computational divide between agent-based models of complex biological systems and noisy experimental data. In our approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), we quantify the uncertainty in the relationship between agent-based model inputs and surrogate model parameters, and between surrogate model parameters and experimental data. In this way, surrogate model parameters serve as intermediaries between agent-based model input and data, making it possible to use them for calibration and uncertainty quantification of agent-based model parameters that map directly onto an experimental data set.
