Pancreatic cancer is an aggressive type of neoplasia characterized by rapid progression and limited therapeutic options, making it a challenging target for both experimental and computational investigation. Mathematical and computational models provide a systematic framework to study tumor dynamics and treatment-related mechanisms under controlled and reproducible conditions. In this work, we present an agent-based pancreatic tumor model developed using BioDynaMo, an open-source agent-based simulation platform [1], to investigate in silico pancreatic cancer treatment and immune response. Cancer cells are represented as discrete agents governed by rule-based dynamics derived directly from a reference mathematical model of pancreatic cancer growth. The model incorporates key biological processes relevant to tumor progression, including tumor cell proliferation and death, as well as immune-mediated effects on tumor cell viability, following the mechanisms described in the reference formulation. The agent-based implementation enables spatial simulations in a three-dimensional environment, where diffusion of substances and mechanical interactions between cells can be represented. The primary objective of this study is to replicate key behaviors reported in published computational studies (e.g., [2]), serving as a validation step of the agent-based modelling (ABM) procedure. BioDynaMo’s simulation results are compared against reference in silico data from the mathematical model, including the temporal evolution of the tumor cell population for different parameter configurations governing tumor growth and immune-related effects. The ABM simulations reproduce the qualitative trends observed in the reference study, indicating consistency between the mathematical and agent-based representations. In addition, a sensitivity analysis is performed to assess the impact of selected model parameters governing tumor growth and immune-related effects on the resulting tumor dynamics. Overall, this work demonstrates the capacity of our in silico model using BioDynaMo to reproduce key behaviors of established mathematical models of pancreatic cancer dynamics and therapy. The results support the consistency of the ABM formulation with the reference computational study, while offering spatial representations of local interactions.