We have developed a computational model of the post-ganglionic sympathetic neuron (SN) that can simulate drug effects that reduce sympathetic hyperactivity, allowing high-efficiency analysis of potential drug treatments that can be further investigated experimentally. This computational model was calibrated to patch-clamp data from the spontaneous hypertensive rat (SHR). We validated the simulated response to changes in input current, and M-type potassium channel up-regulation to demonstrate the accurate response to drug effects. We accurately predicted firing frequency, resting membrane potential, and peak membrane potential for all experiments. This demonstrates that the model can be used for investigating drug effects and - once it has been coupled with cardiac electrophysiology (EP) models - will have the potential to improve cardiac in silico trials to account for sympathetic activity. The model predicts norepinephrine (NE) release at the neuro-cardiac junction, making it suitable to be coupled to cardiomyocyte models for predicting cardiac EP response. This SN model forms the foundation for a coupled SN–cardiac EP model that will be used to predict the response to treatments of dysautonomia in rare diseases such as Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT).