Synergy Maps: exploring compound combinations using network-based visualization
© Lewis et al. 2015
Received: 29 April 2015
Accepted: 22 July 2015
Published: 1 August 2015
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.
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.
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.
KeywordsCompound combinations Mixtures Synergy Visualization Network Dimensionality reduction
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 pharmaceutical treatment options in terms of increased efficacy  and therapeutically relevant selectivity , whilst reducing the risk of toxicity  and side-effects . 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 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 . Enrichment analysis of molecular and pharmacological properties predicted several combinations to be synergistic, 69% of which were subsequently verified in the literature . Clinical side effect annotations have been used to predict effective combinations , 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 . Various network approaches (such as the Stochastic Block Model  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 , and mechanisms of action of many known non-additive drug combinations have been deduced . 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) , 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 . Other all-pairs combinatorial datasets include a 90 compound set (consisting of drugs and probes) assayed against the HCT116 colon cancer cell line , a set of 11 anticancer drugs tested also tested against HCT116 , a set 31 antifungal compounds assayed against S. cerevisiae [24, 25], and an assay of 22 antibiotics against Escherichia coli . Each of these datasets measure dose response surfaces , 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 NCI-60 cell lines .
Visualizing large numbers of combinations
Heatmaps are useful as an uncluttered static presentation of data. It is possible to identify disproportionately synergistic compounds and also compounds that behave similarly if clustering such as in Cokol et al.  and Fig. 1 is applied. Additionally, relevant dose–response matrices may be superimposed [11, 23, 25] to reveal different shapes of response surfaces, which may encode information of underlying biological network topology [11, 30, 31]. A drawback is that little information about the actual compounds are encoded—they may be ordered according to a physicochemical property, but this is limits further possible insight into the dataset. Furthermore, for a large dataset (for example over a hundred compounds), such as those produced using high-throughput techniques , the heatmap quickly becomes cluttered and individual compounds become difficult to identify.
Hence, an improvement in chemical property representation for the visualization of compound combination screens is still very much desirable, which is the objective of the current work.
Chemical property visualization
Compounds have traditionally been represented under a descriptor space using a dimensionality reduction algorithm as a scatter plot; a common example is Principle Component Analysis (PCA)  applied to physicochemical descriptors. A state-of-the-art equivalent might be the use of Student’s t-distributed Stochastic Neighbour Embedding (t-SNE)  on proprietary descriptors . In this way, compounds may be easily compared according to their properties or features; adjacent compounds tend to share properties and behaviour in the descriptor space in question.
In this communication, we introduce a novel type of visualization for combination datasets, named “Synergy Maps”. Synergy Maps combine network and descriptor space representation to yield an information dense presentation of a combination dataset. Specifically, the approach positions the nodes of a drug–drug interaction graph in two-dimensional space using the techniques referred to in the previous section; in this way, synergistic interactions can be straightforwardly related to trends in compound properties, and thus hypotheses for the origins of the synergy might be more quickly proposed. We also introduce an interactive implementation, which enables the generation of synergy maps for novel combination datasets, and allows for exploration of synergy under different spaces, metrics and datasets. Source code is provided as a GitHub repository.
As an example, we produce synergy maps for a combination dataset of 56 antimalarials tested against P. falciparum, and detail a quick analysis of the resultant maps.
An input dataset should consist of compound data in the form of a Structure-Data File (SDF), and data associated with their combinations (including calculated synergy metrics) in the form of a comma separated values (CSV) file (examples provided with the repository). A script is then written (or a default one used), specifying the descriptors, dimensionality reduction techniques and synergy metrics to employ in generating the processed file (example scripts provided with the repository).
A previously collected all pairs combination dataset of 56 compounds tested against P. falciparum  was selected as an example dataset to concretely illustrate the technique. Each combination was tested in a 6 × 6 dose–response matrix, varying the concentration of each compound on each axis. The change in growth inhibition was measured at each dose combination, yielding a response surface. From this, 9 different synergy metrics  were evaluated for all 1,540 combinations. These were then preprocessed into the appropriate input format.
Descriptors calculated for the compounds, which were used for later visualization in Synergy Maps
Due to the relatively small chemical space spanned by the 56 compounds, an additional 175 diverse compounds from MIPE  were temporarily added to the dataset, to diversify the space covered, and so allow for a better and more consistent dimensionality reduction step. This may not be necessary for a larger and more diverse compound set, but in practice made the resultant plots more reproducible and transferable (this was especially the case for t-SNE, which has a non-convex objective function, and thus converges to different solutions each time it is run. It also allowed for a higher perplexity (roughly the expected density of neighbors) to be set, which prevents artificially large gaps opening in the dataset).
Synergy metrics calculated for the NCATS malaria dataset
Negative based-10 logarithm of Gamma, from Cokol et al. 
The median of the sum of the differences between the combination responses and the single agent responses
The number of combinations in a block that show a better combination responses than both the corresponding single agents
Negative of ExcessHSA, from Lehár et al. 
Negative of ExcessCRX, from Lehár et al. 
LS3 × 3
The minimum value of the sum of the deviations from the HSA model are evaluated on all 3 × 3 submatrices of the response matrix (excluding the single agent row and column)
Negative based-10 logarithm of Beta, from Cokol et al. 
Results and discussion
Whilst the purpose of this paper is simply to introduce a novel visualization technique rather than analyze the resulting networks, it is possible to illustrate a few observations that may be made; these could be investigated further in subsequent assays. Firstly, we can see that compounds annotated as histone deacetylase (HDAC) inhibitors, which are clustered in the north-east of the Fig. 6, appear to be the most likely compounds in the dataset to be synergistic, and specifically with the compounds in the center (these are annotated with diverse modes of action, but often were kinase or phosphatase inhibitors). This property has been reported in the literature, where the HDAC inhibitor trichostatin A was found to interact synergistically with geldanamycin, an Hsp90 inhibitor , in P. falciparum. Interestingly, NVP-AUY922, an Hsp90 inhibitor included in the dataset, clustered to the centre; this is likely where geldanamycin would also be placed due to their similar annotated modes of action. This result would be in agreement with the observed trend and suggest that the method might yield some predictive power for unknown combinations. In contrast to this, PI3K inhibitors are shown to exhibit in general disproportionately more antagonism with the other compounds in the dataset. Whilst these observations are by no means reliable by themselves, they may form a basis for further study, and provide an example in how this type of visualization may prove a useful first step in the analysis of pairwise combination data.
Synergy Maps, a novel method for visualization of a combination data set was presented, integrating combination-based information in a network, with compound-based information using a dimensionality reduced scatter-plot. An accompanying interactive visualization tool was also introduced, which enables fast and simple exploration and presentation of combination data. An all-pairs combination dataset assayed against P. falciparum was analyzed as an example, identifying several properties already reported in the literature.
Availability and requirements
Project name: Synergy Maps.
Project home page: https://www.github.com/richlewis42/synergy-maps.
Operating system(s): Platform independent/Google Chrome.
flux balance analysis
Principal Components Analysis
Multi Dimensional Scaling
t-distributed Stochastic Neighbour Embedding
comma separated values
RG produced the data, and carried out the preliminary analysis. RL conceived of and designed the software, carried out the data analysis and drafted the manuscript. AB, TK and RG helped draft the paper. All authors read and approved the final manuscript.
Azedine Zoufir, Yasaman Kalandar Motamedi, Dan Mason and Krishna Bulusu are thanked for their advice and work in the area, and the rest of the Bender Group for helpful feedback on the layout and design of the software. RL thanks EPSRC for funding. TK is supported by a fellowship in computational biology at The Genome Analysis Centre, in partnership with the Institute of Food Research, and strategically supported by BBSRC. AB thanks the European Research Commission for funding (ERC Starting Grant 2013 MIXTURE).
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
Open AccessThis 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.
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