Pharmacokinetic/Pharmacodynamic (PK/PD) modeling plays a pivotal role in novel drug development. Previous population-based PK/PD models encounter challenges when customized for individual patients. We aimed to investigate the feasibility of constructing a pharmacodynamic model for different phases of individual breast cancer pharmacodynamics, only leveraging limited data from early phases. To achieve that, we introduced an innovative approach, Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for multi-task, e.g., breast cancer tumor dynamics and progression-free survival predictions. To validate the DN-ODE approach, we conducted experiments with early-phase clinical trial data from the amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2) to predict pharmacodynamics in the later phase (AMEERA 3). Empirical investigations confirmed the efficacy of the DN-ODE, surpassing alternative PK/PD methodologies. Notably, we also introduced visualizations for each patient, demonstrating that the DN-ODE recognizes diverse tumor growth patterns (responded, progressed, and stable). Therefore, the DN-ODE model offers a promising tool for researchers and clinicians, enabling a comprehensive assessment of drug efficacy, identification of potential