RT Journal Article SR Electronic T1 Harnessing Big Data for Systems Pharmacology JF bioRxiv FD Cold Spring Harbor Laboratory SP 077115 DO 10.1101/077115 A1 Lei Xie A1 Eli J. Draizen A1 Philip E. Bourne YR 2016 UL http://biorxiv.org/content/early/2016/09/23/077115.abstract AB Systems pharmacology aims to holistically understand genetic, molecular, cellular, organismal, and environmental mechanisms of drug actions through developing mechanistic or predictive models. Data-driven modeling plays a central role in systems pharmacology, and has already enabled biologists to generate novel hypotheses. However, more is needed. The drug response is associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological networks, and is dependent on pharmacokinetics. Thus, in order to gain a comprehensive understanding of drug actions, systems pharmacology requires integration of models across data modalities, methodologies, organismal hierarchies, and species. This imposes a great challenge on model management, integration, and translation. Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology. It will allow systems pharmacology modeling to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.