Diseases associated with sympathetic hyperactivity need improved treatments that target and account for the sympathetic nervous system. We have developed a computational model of the postganglionic 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, and membrane potential features for all experiments. The model can now be used for investigating drug effects and - once it has been coupled with cardiac electrophysiology (EP) models - will 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 to treatments of dysautonomia in rare diseases such as Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT).