Drug addiction (or dependence) to therapy in cancer describes the paradoxical condition in which a resistant tumor subpopulation becomes both resistant and dependent on treatment for survival. This phenomenon, previously observed in unresectable melanoma treated with MAPK inhibitors, reveals a reduced tumor growth rate during drug holidays — an evolutionary cost that can be strategically exploited. Such observations align with the principles of adaptive and evolutionary therapy, which aim to design treatment schedules that steer and exploit the cost of resistance to reduce its burden though optimally constructed treatment strategies. In this work, we investigate drug resistance and addiction in ALK-positive Anaplastic Large Cell Lymphoma (ALCL) exposed to various ALK inhibitors (ALKi), which are used as salvage therapy for chemotherapy-resistant disease. Using a patient-derived cell line model from treatment-naïve ALCL, we induced resistance by gradually escalating ALKi concentrations over a period of more than six months. Parallel experiments tracked the evolution of drug response through longitudinal dose-response curves (DRCs), both before and after resistance and addiction emerged. Non-monotonic DRCs confirms the existence of drug-addiction to ALKi, consistent for all 3 inhibitors tested. Guided by these data, we developed a mathematical model to capture the temporal evolution of DRCs and to test how therapy design could exploit addiction in ALCL. Calibrated on experimental measurements, the model simulated tumor growth under alternative treatment strategies. Our results reveal that while continuous therapy drives resistance and tumor regrowth within ~150 days, intermittent schedules maintain long-term control for over 300 days. Furthermore, simulations suggest an adaptive strategy: switching from continuous dosing to intermittent therapy precisely when tumor burden reaches a minimum. This approach, grounded in evolutionary principles, prevents resistant populations from dominating and leverages the fitness cost of drug addiction. Additional experiments under intermittent dosing with naïve and resistant lines (both mono and co-culture) provide a preliminary validation of the effectiveness of treatment holidays to exploit ALKi drug-addiction. Moreover, they provide an opportunity to reparametrize the model to account for the effect of tumor heterogeneity. While further experimental validation is needed to validate dynamic, adaptive strategies in ALCL, our study demonstrates that integrating mathematical modeling with empirical DRCs enables a quantitative framework for predicting the evolutionary dynamics of resistance and addiction. Importantly, these results highlight the potential of adaptive and evolutionary therapy in ALCL, where optimized treatment schedules can reduce tumor burden, delay progression, and exploit the vulnerabilities created by resistance mechanisms.