Speaker
Description
Resistance to the anti-mitotic drug docetaxel is a major challenge in the treatment of solid tumors, including prostate and pancreatic cancers. The stress-response protein NUPR1 has been identified as a key molecular driver of docetaxel resistance, suggesting that inhibition of NUPR1 may restore treatment sensitivity and enable effective combination therapy. In this talk, I develop mathematical models that incorporate the mechanisms of action of docetaxel and relevant NUPR1 pathways to study tumor response to combined docetaxel and NUPR1-targeted therapy. The models are informed by preclinical data, including in vitro prostate cancer cell-line experiments and in vivo pancreatic cancer xenografts, and capture tumor growth dynamics and treatment-induced cell death. Using these calibrated models, we quantify potential synergy between therapies and extend the analysis to heterogeneous virtual populations that represent variability in tumor growth and drug response. This framework enables systematic exploration of treatment schedules and patient heterogeneity, allowing us to assess when NUPR1-targeted combination therapy is likely to be successful and to identify subpopulations that may benefit most. These results illustrate how mechanistic modeling and virtual populations can support the evaluation and design of combination therapies aimed at overcoming chemotherapy resistance.
