INGOT: Towards network-driven in silico combination therapy

Combination therapy, where several drugs interact with multiple targets, holds tremendous promise for effective clinical outcomes in the management of chronic, complex diseases such as cancer. In this paper, we take a step towards this grand goal by laying out the vision of a novel in silico, data-driven combination therapy framework called ingot for complex network diseases. Given the genomic and proteomic profiles of a patient population, it automatically predicts “optimal” set of synergistic drug combinations and corresponding dosages, which can potentially achieve the therapeutic goal while minimizing any off-target effects. Towards this goal, we present the architecture of ingot and discuss various non-traditional design challenges and innovative features. Specifically, in ingot, a disease-related probabilistic signaling network (psn) is constructed by integrating publicly-available disease-specific signaling networks with expression data. Next, topology and dynamics of the psn, which can be noisy and incomplete, are analyzed as a whole using probabilistic network analytics techniques to identify promising target combinations with desirable properties (e.g., synergistic in nature, good efficacy and minimum off-target effect) to regulate the activities of key disease-related molecular players. Finally, optimal candidate drug combinations to modulate these targets are predicted by integrating and analyzing drug information (e.g., DrugBank) with the target nodes. Successful realization of this framework can result in an effective platform for in silico screening of drug combinations in a rational way, by aiding early discovery of suitable combination therapy and guiding the design of further in vitro and in vivo experiments.