Synergy Maps: exploring compound combinations using network-based visualization

Lewis et al. J Cheminform (2015) 7:36 DOI 10.1186/s13321-015-0090-6 Open Access SOFTWARE Synergy Maps: exploring compound combinations using network‑based visualization Richard Lewis1 , Rajarshi Guha2, Tamás Korcsmaros3,4 and Andreas Bender1* Abstract Background: The phenomenon of super-additivity of biological response to compounds applied jointly, termed synergy, has the potential to provide many therapeutic benefits. Therefore, high throughput screening of compound combinations has recently received a great deal of attention. Large compound libraries and the feasibility of all-pairs screening can easily generate large, information-rich datasets. Previously, these datasets have been visualized using either a heat-map or a network approach—however these visualizations only partially represent the information encoded in the dataset. Results: A new visualization technique for pairwise combination screening data, termed “Synergy Maps”, is presented. In a Synergy Map, information about the synergistic interactions of compounds is integrated with information about their properties (chemical structure, physicochemical properties, bioactivity profiles) to produce a single visualization. As a result the relationships between compound and combination properties may be investigated simultaneously, and thus may afford insight into the synergy observed in the screen. An interactive web app implementation, available at http://richlewis42.github.io/synergy-maps, has been developed for public use, which may find use in navigating and filtering larger scale combination datasets. This tool is applied to a recent all-pairs dataset of anti-malarials, tested against Plasmodium falciparum, and a preliminary analysis is given as an example, illustrating the disproportionate synergism of histone deacetylase inhibitors previously described in literature, as well as suggesting new hypotheses for future investigation. Conclusions: Synergy Maps improve the state of the art in compound combination visualization, by simultaneously representing individual compound properties and their interactions. The web-based tool allows straightforward exploration of combination data, and easier identification of correlations between compound properties and interactions. Keywords: Compound combinations, Mixtures, Synergy, Visualization, Network, Dimensionality reduction Background Compound combinations have recently received much interest, as they afford a number of advantages as therapeutics compared to single agent treatments across a wide range of disease areas [1–4]. The phenomenon of super-additivity of the therapeutic effect of a combination, known as synergy, has the potential for improved *Correspondence: 1 Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK Full list of author information is available at the end of the article pharmaceutical treatment options in terms of increased efficacy [5] and therapeutically relevant selectivity [6], whilst reducing the risk of toxicity [7] and side-effects [8]. Two recent reviews are available on the topic [9, 10]. However, how to determine which compound combinations exhibit a desired form of synergy in a particular case is by no means clear, and the effect of multiple bioactive compounds in parallel is overall rather poorly understood. Synergy in a combination is due to not purely additive interaction between the biological functions of © 2015 Lewis et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lewis et al. J Cheminform (2015) 7:36 the component compounds. Progress has been made in attempts to model synergy, usually by attempting to discover these interactions. For example, models incorporating flux balance analysis (FBA) have been used to correctly predict synergistic interactions in Saccharomyces cerevisiae [11]. Enrichment analysis of molecular and pharmacological properties predicted several combinations to be synergistic, 69% of which were subsequently verified in the literature [12]. Clinical side effect annotations have been used to predict effective combinations [13], and information from multiple domains have been integrated into a Probability Ensemble Approach to predict both efficacy and adverse effects of combinations with high predictive power [14]. Various network approaches (such as the Stochastic Block Model [15] and the Prism algorithm [15, 16]) have been used to infer novel interactions from large incomplete drug interaction databases such as DrugBank [17, 18]. Biological network topologies of drug targets that lead to synergy have been identified through network modelling [19], and mechanisms of action of many known non-additive drug combinations have been deduced [20]. However, these models usually require heavily annotated data (such as with ATC codes, protein targets or side effect data)—a complete understanding of the origins and repercussions of synergy has not yet in general been achieved, and thus significant further work is needed, both experimental and in silico. To this end, an experimental strategy for measuring synergy has been assaying all pairwise combinations for a relatively small compound library. A recently published example of this type of dataset is the DREAM Drug Sensitivity Challenge (subchallenge 2) [21], in which all combinations of 14 compounds were tested on the LY3 lymphoma cell line. The degree of synergy for each combination was indicated by the difference in growth inhibition observed by experiment from that predicted under the Bliss Independence model [22]. Other all-pairs combinatorial datasets include a 90 compound set (consisting of drugs and probes) assayed against the HCT116 colon cancer cell line [11], a set of 11 anticancer drugs tested also tested against HCT116 [23], a set 31 antifungal compounds assayed against S. cerevisiae [24, 25], and an assay of 22 antibiotics against Escherichia coli [16]. Each of these datasets measure dose response surfaces [5], and derive synergy metrics from those surfaces (see original papers for examples). Whilst this is currently a reasonable selection in terms of dataset size, compound variety and assay type, there is potential for many more experiments—an exciting prospect is an upcoming National Cancer Institute Combination Screen of approximately 100 anti cancer drugs tested pairwise against the 59 NCI60 cell lines [26]. Page 2 of 11 Visualizing large numbers (...truncated)