Mathematical models of the cardiac action potential are increasingly used to investigate the effects of pharmacological compounds. Nevertheless, most existing studies analyze drug responses only under baseline conditions, overlooking the impact of genetic variants such as long QT syndromes as well as the intrinsic variability observed in human cardiomyocytes. In this work, we analyze the effects of a set of IKr-blocking drugs on populations of models representing both healthy and long QT conditions. We constructed four populations of models, calibrated using experimental measurements to capture physiological variability in control cells and to represent the three most prevalent long QT syndromes: types 1, 2, and 3. The influence of 12 IKr inhibitors on action potential duration was assessed in each population using both static and dynamic drug–channel interaction formulations. The simulations correctly reproduce the experimentally observed action potential prolongation, with long QT type 2 showing the strongest effect, followed by types 3 and 1. Notably, drug-induced prolongation and the incidence of early afterdepolarizations were greater in the LQT1 population. In addition, dynamic drug models led to statistically significant differences compared with static approaches. Overall, these results deepen our understanding of drug–disease interactions in long QT syndromes and highlight the relevance of incorporating binding kinetics to improve predictions of drug effects.