While the approach described in this paper was successful in using connectivity information to link the drugs to the processes that they are targeting and finding combinations that affect similar processes we see the limit of the standard Loewe mathematical model becomes a concern when we start to work outside the original specifications of the model when trying to predict drug synergy. That is when we start applying the model to conditions different from two drugs sharing the same target. The Loewe model makes the assumption that two drugs are both acting upon a common target. A more appropriate approach would be to use a Generalized Concentration Addition (GCA) model that handles common processes better than a standard CA approach like the Loewe-Additivity Model. In order to further develop and refine this method of combination prediction for drug synergy, it would be interesting to look at a mathematical model more suited for our combinations. One technique that would be possible would be to use the GCA model since it improves upon the more basic CA model . In follow up experiments, we would prefer to use find transcriptomics data to see if the pathways which contain the targets are also affected by the drugs of interest. Labib et al. performed an interesting study to predict hazards associated with complex mixtures of polycyclic aromatic hydrocarbons. They did this by using the GCA model in combination with a pathway benchmark done. They first selected pathways relevant for cancer formation and then calculated the dose-response for each individual compound and pathways using CA, IA, and GCA models and compared their predictions to observations. A similar approach could be used as a followup for our combination predictions and would require transcriptomics data for the drugs at various concentrations.