Transcriptional response networks for elucidating mechanisms of action of multitargeted agents

• Published in: Drug discovery today 2016-07, Vol.21 (7), p.1063-1075
• Main Authors: Kibble, Milla, Khan, Suleiman A., Saarinen, Niina, Iorio, Francesco, Saez-Rodriguez, Julio, Mäkelä, Sari, Aittokallio, Tero
• Format: Article
• Language: English
• Subjects: Animals; Computational Biology; Drug Discovery; Gene Regulatory Networks; Humans
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/j.drudis.2016.03.001

•Elucidation of a compound's target mechanisms is key to predicting its phenotypic effects.•Computational network pharmacology models provide hypotheses on multi-target mechanisms.•Data-driven models can lead to unbiased findings and novel drug development paths.•Model predictions reduce the number of in vitro and in vivo target validation experiments.•These models also enable systematic discovery of drug repositioning opportunities. Systems-level drug response phenotypes combined with network models offer an exciting means for elucidating the mechanisms of action of polypharmacological agents, including multitargeted natural products. Drug discovery is moving away from the single target-based approach towards harnessing the potential of polypharmacological agents that modulate the activity of multiple nodes in the complex networks of deregulations underlying disease phenotypes. Computational network pharmacology methods that use systems-level drug–response phenotypes, such as those originating from genome-wide transcriptomic profiles, have proved particularly effective for elucidating the mechanisms of action of multitargeted compounds. Here, we show, via the case study of the natural product pinosylvin, how the combination of two complementary network-based methods can provide novel, unexpected mechanistic insights. This case study also illustrates that elucidating the mechanism of action of multitargeted natural products through transcriptional response-based approaches is a challenging endeavor, often requiring multiple computational–experimental iterations.