Drug combinations can induce an additive effect; in which the therapeutic outcome is equal to the effect of monotherapy; an antagonistic effect which reduces therapeutic effects or they can lead to a synergistic effect in which the therapeutic effects are enhanced . Synergistic combinations may offer advantages over mono-drug therapies, because the therapeutic effect in synergistic drugs is greater than simply adding the two drugs' effects together . In addition, reduced drug resistance may occur due to synergistic drug combinations, because each individual drug needs a lower dose. Furthermore, a drug compound which normally does not induce an effect on its own could potentially have an effect when in combination with another drug, giving rise to a larger range of possible drug treatments. Due to the large number of possible drug combinations, large and complex data make studying every possible combination difficult. Important developments include the use of genomic data in drug discovery, the sharing of clinical-trial data, as well as the increased availability of data from claims and patient registries . This new paradigm has been referred to as data-driven medicine , an area which explores big data, that is, datasets that are too large and complex for traditional data processing applications to work with. Thus, large amounts of data encourage the use of advanced computational analytical methods which enable researchers to take into account a wide set of data beyond those generated in standardized clinical trials and analyzed through classical biostatistics. Harnessing the power of big data can potentially improve success rates in the outcomes of patients receiving treatment as well as enabling pharmaceutical companies to enhance the productivity of their research and development . With the use of big data, researchers can pursue more computational approaches to drug development such as the one described in this paper that would be predicted to have a synergistic outcome or not.