Multiscale computational models can provide systemic evaluation and prediction of neuropharmacological drug effects. To date, little computational modeling work has been done to bridge from intracellular to neuronal circuit level. A complex model that describes the intracellular dynamics of the presynaptic terminal of a serotonergic neuron has been previously proposed. By systematically perturbing the model's components, we identify the slow and fast dynamical components of the model, and the reduced slow or fast mode of the model is computationally significantly more efficient with accuracy not deviating much from the original model. The reduced fast-mode model is particularly suitable for incorporating into neurobiologically realistic spiking neuronal models, and hence for large-scale realistic computational simulations. We also develop user-friendly software based on the reduced models to allow scientists to rapidly test and predict neuropharmacological drug effects at a systems level.