Diseases, co-morbidities, and patients responses to certain therapies are the result of a complicated interplay of numerous factors. The relationships defining how this intricate network of inputs leads to various outputs is extremely difficult to derive using conventional means. Thanks to Big Data, high-level applied mathematics and computers, the EU-funded DC-ren project (Drug combinations for rewriting trajectories of renal pathologies in type II diabetes) is developing a tool that will take all this and more into account, and enable optimised personalised drug therapies for significantly enhanced outcomes. The team is focusing on diabetic kidney disease, a common co-morbidity of type 2 diabetes often accompanied by cardiovascular disease. Currently, the drug cocktails targeting it produce highly varied responses. Thanks to its vast patient database, advanced experimental techniques and dynamical systems theory, DC-ren is developing a completely new and widely applicable computational framework for decision support.