Marco computacional de multiphysics integrado con geometría derivada del paciente: Optimización computacional de la...

The immune checkpoint inhibitor (ICB) resistance of prostate cancer is due to its peculiar tumor microenvironment (TME), dense collagen stromal and disorganized vascularity, increased interstitial fluid pressure (IFP), blocked drugs and also immune cell penetration, and fostered immunosuppression. None of the generic computational models considered all these factors together in their modelin before. To address these problems, we developed a patient-specific multiphysics framework that integrates three-dimensional (3D) magnetic resonance imaging (MRI)-derived tumor geometries of prostate from the Cancer Imaging Archive (TCIA), TME normalization procedures (vascular and stromal), nanocarrier-based PD-1/anti-PD-L1 delivery, with the simulation time extended to 50 days, and long-term responses due to immune memory or recurrence, beyond those observed in the previously described less than 30-day simulations. Vascular normalization optimized vessel permeability and functional density, while stromal normalization reduced extracellular matrix (ECM) stiffness, effectively reprogramming the TME across all patient tumors. In this simulation, the value of the IFP reduced by over 70 % (from 1458-1484 Pa to 438-445 Pa), and the interstitial fluid velocity (IFV) increased by 2-3 folds, with a mechanical stress reduction of 42 %. This permissive environment facilitated the tumor penetration of nano-ICB, allowing therapeutic concentration levels to be reached in the tumor interstitium (from 0.0039 nmol/ml to 0.0058 nmol/ml). Immune system optimization, involving increasing cytotoxic T cell (CD8⁺ T cell) entry (from 150 day^-1 to 300 day^-1) and regulatory T cells (Treg) rate of death (from 0.02 day^-1 to 0.05 day^-1), drove the fraction of killed cancer cells (FKCs) to 0.52 in Tumor 3 and 0.84 in Tumor 2. Model validation confirmed numerical robustness via mesh (184,254 elements) and time-step (0.1 min) independence, with less than 2 % variation in key outputs (IFP, IFV, tumor volume, and FKC). The presented study moves the frontiers of computational oncology by relating the anatomical specifics of the patient to the mechanics of the TME, on one hand, and nano-immunotherapy, on the other, advancing beyond the simplifications of common geometric representations and one-component models. It provides a clinically applicable platform for the optimization of individualized treatment of prostate cancer based on the prediction of the effectiveness of synergies between TME normalization and nano-ICB to enhance anti-tumor efficacy.