Botulinum neurotoxin is a pharmaceutical treatment used for an increasing number of neurological and non-neurological indications, symptoms and diseases. Despite the wealth of clinical reports that involve the timing of the therapeutic effects of this toxin, few studies have attempted to integrate these data into unified models. Secondary reactions have also been examined including the development of adverse events, resistance to repeated applications, and nerve terminal sprouting. Our primary intent for conducting this review was to gather relevant pharmacodynamic data from suitable biomedical literature regarding botulinum neurotoxins via the use of automated data-mining techniques. We envision that mathematical models will ultimately be of value to those who are healthcare decision makers and providers, as well as clinical and basic researchers. Furthermore, we hypothesize that the combination of this computer-intensive approach with mathematical modeling will predict the percentage of patients who will favorably or adversely respond to this treatment and thus will eventually assist in developing the increasingly important area of personalized medicine.