Predicting Multi-Drug Inhibition Interactions based on Signaling Networks and Single Drug Dose-Response Information

Abstract

Inhibiting multiple pathways/targets with combination drug therapies is widely used to maximize therapeutic benefit in many diseases. Therapeutic success can often be improved by concurrently and/or sequentially inhibiting multiple pathways/targets by combining drugs with different mechanisms of action. It is critical to understand potential drug interactions in developing and optimizing new multidrug regimens. We used data from experiments of single drugs (and few combinations) and existing signaling network knowledge from sources such as KEGG, to develop a statistical rescaling model to describe the effects of drugs on network topology. The model comprises a Hill equation for signals arriving at each receptor, a generic enzymatic rate equation to transmit signals among connecting genes, and a regression model to represent the cumulative effect of genes implicated in activation of the cell death machinery. We performed in silico experiments to derive a global sensitivity index of each term in the functional ANOVA of the dose-response model by generating doses of the drugs with the Quasi Monte-Carlo method. Only terms with large (principal) global sensitivity indices remain in the functional dose-response model. Thus, we reduce a high dimensional problem into one that can be managed experimentally. The model generates an index that indicates potential interactions of the drugs and the principal combinations, those associated with large global sensitivity indices, can be considered as candidates for further experimental validation.