Drug Discovery Today Volume 21, Number 7 July 2016 REVIEWS 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. Transcriptional response networks for FOUNDATION REVIEW elucidating mechanisms of action of multitargeted agents Reviews 1 1 2 Milla Kibble received her Milla Kibble , Suleiman A. Khan , Niina Saarinen , PhD in pure mathematics 3 3,4 2 from University College Francesco Iorio , Julio Saez-Rodriguez , Sari Ma¨kela¨ and London in 2000. She 1,5 subsequently worked at Tero Aittokallio Numbercraft Ltd., using analytics techniques to gain 1 Institute for Molecular Medicine Finland (FIMM), Biomedicum Helsinki 2U, Tukholmankatu 8, University of information from large data sets. Since then, she has Helsinki, Helsinki 00014, Finland 2 combined teaching of mathematics and statistics with Institute of Biomedicine, Turku Center for Disease Modeling & Functional Foods Forum, University of Turku, Turku research into probability and graph theory applications 20014, Finland to biological data in the Biomathematics Group at 3 European Molecular Biology Laboratory – European Bioinformatics Institute, Wellcome Trust Genome Campus, Turku University and the Stochastics Group at Aalto Cambridge CB10 1SD, UK University. Milla joined the Institute for Molecular 4 Medicine Finland (FIMM) as a senior researcher in 2014 Joint Research Centre for Computational Biomedicine (JRC-COMBINE) – RWTH Aachen University, Faculty of and her research focuses on network pharmacology Medicine, D-52074 Aachen, Germany 5 approaches to elucidate mechanisms of action and Department of Mathematics and Statistics, Quantum, University of Turku, Turku 20014, Finland efficacy of drugs and their combinations, with a particular interest in natural products. Suleiman Ali Khan received his PhD in Drug discovery is moving away from the single target-based approach computer science from Aalto University in 2015, towards harnessing the potential of polypharmacological agents that with a primary focus on modulate the activity of multiple nodes in the complex networks of statistical machine learning and bioinformatics, in deregulations underlying disease phenotypes. Computational network particular the application and development of novel pharmacology methods that use systems-level drug–response phenotypes, Bayesian multiview methods to model structural such as those originating from genome-wide transcriptomic profiles, have responses of drugs. He subsequently joined FIMM as a postdoctoral researcher and has been working on the proved particularly effective for elucidating the mechanisms of action of identification of drug response factors using integrative and predictive machine-learning methods, with a multitargeted compounds. Here, we show, via the case study of the natural specific emphasis on combining information from product pinosylvin, how the combination of two complementary multiple genomic data sets. His current research interests include computational biology, medicine, and network-based methods can provide novel, unexpected mechanistic statistical machine learning. insights. This case study also illustrates that elucidating the mechanism of Tero Aittokallio received his PhD in applied action of multitargeted natural products through transcriptional mathematics from the response-based approaches is a challenging endeavor, often requiring University of Turku in 2001. He did his multiple computational–experimental iterations. postdoctoral training in the Systems Biology Lab at the Institut Pasteur (2006– Introduction 2007), where he focused on network biology applications using high-throughput Over the past few decades, drug discovery has adopted a target-based approach, searching for experimental assays. Dr Aittokallio then launched his compounds that target a specific disease-causing gene product [1]. Perhaps the most successful independent career as a principal investigator in the Turku Biomathematics Research Group in 2007, and example of the target-based approach was the discovery of imatinib, a BCR-ABL tyrosine kinase received a 5-year appointment as an Academy of inhibitor (TKI), for the treatment of several cancers, including Philadelphia chromosome-positive Finland Research Fellow (2007–2012). In 2011, he started as EMBL Group Leader at FIMM, where his chronic myeloid leukemia (CML) [2]. However, recently, the paradigm of searching for highly research group is developing systems medicine solutions for biomedical problems, with a particular interest in personalized medicine. Corresponding author: Kibble, M. (milla.kibble@helsinki.fi) 1359-6446/ß 2016 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drudis.2016.03.001 www.drugdiscoverytoday.com 1063 REVIEWS Drug Discovery Today Volume 21, Number 7 July 2016 GLOSSARY Here, we describe how the combination of two complementary computational network pharmacology methods, namely Mode of Batch effect the similarity of gene expression or other Action by Network Analysis (MANTRA) [11] and Group Factor molecular profiles observed for unrelated stimuli in cells Analysis (GFA) [12], can provide novel, unexpected MoA predic- grown or processed simultaneously. Drug network a network in which drugs are represented by tions. As a specific case, we illustrate the operation of these Reviews nodes and nodes are connected if two drugs are similar in a methods in the context of multitargeted natural products. Al- predefined manner (here, in terms of their transcriptional though less popular during the era of target-based drug discovery, response). natural products still make a substantial contribution to current FOUNDATION REVIEW Drug repositioning the discovery of new uses for approved new drugs [13–16]. Recent reviews have highlighted the largely drugs, which have well-known safety and pharmacokinetic untapped potential of network pharmacology approaches in profiles [23]. natural product research [17–20]; we chose as our case study a Gene expression profile a list of probe set identifiers, ranked according to differential gene expression in a particular relatively little-researched natural polyphenol, pinosylvin (3,5- cellular context, with the most upregulated probe sets at the dihydroxy-trans-stilbene). Pinosylvin has been shown to have top of the list. antiproliferative and proapoptotic efficacy on prostate cancer cells Mechanism of action (MoA) the set of cellular targets or in vitro and in vivo [21], but its MoA has so far been poorly other mechanisms of a compound that produce its understood. In this study, we show that the true value of the pharmacological effect in a given cellular context. data-driven network-based methods is their potential to lead to Network biology systems-level modeling of biological unbiased hypotheses that might not otherwise have been con- processes, pathways, and molecular interactions, rather than focusing on individual players only. ceived and, hence, to truly novel and even surprising findings. Network pharmacology conceptualization of the fact that, in certain cases, to have an effect, drugs must target multiple Transcriptional response networks to elucidate drug pathways in a disease network and/or work synergistically MoA with other drugs. We illustrate the operation of the computational network phar- Synergy the joint action of drugs that when used together macology methods using genome-wide gene expression response increase each other’s effectiveness. See [80] for a discussion profiles as their input. The main public resource for such data is the of the challenges of modeling synergistic interactions. CMap resource, which comprises genome-wide transcriptional responses of various human cell lines to more than 1000 bioactive selective ligands has been questioned, because of high late-stage small molecules (Table 1). Given that our example compound clinical attrition rates, which can be largely attributed to lack of pinosylvin is not included in this resource, we produced a response clinical efficacy and safety [3–6]. With the recognition that many profile for pinosylvin in house (Box 1). Since the advent of the approved drugs are in fact multitargeted, an alternative systems CMap resource, researchers have successfully discovered cellular biology approach to drug discovery is increasing in popularity. The targets of compounds and identified candidates for drug reposi- so-called ‘network pharmacology paradigm’ [7] (see Glossary) tioning by searching for commonalities in the phenotypic makes use of systems-level disease models, aiming at finding responses using the simple idea that, if two drugs elicit similar compounds (or combinations thereof) that can modulate the transcriptional responses, then they could share a common MoA activity of multiple nodes in the complex network of impaired (hence, potentially a therapeutic application) even if directly mechanisms and deregulated interactions underlying a disease acting on different intracellular targets. The original CMap tool phenotype (i.e., the ‘disease network’), either by targeting several compares transcriptional response profiles using a method similar pathways or by working synergistically, potentially with fewer to Gene Set Enrichment Analysis (GSEA) [22], based on the Kol- adverse effects [8,9]. mogorov-Smirnov statistic. Furthermore, computational research- Therefore, an essential part of the modern drug discovery ers have provided an abundance of methods for measuring the