Metabolic Clustering Analysis as a Strategy for Compound Selection

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Metabolic clustering analysis as a strategy for compound selection in the drug discovery pipeline for leishmaniasis Emily G Armitage, Joanna Godzien, Imanol Peña, Ángeles López-Gonzálvez, Santiago Angulo, Ana Gradillas, Vanesa Alonso-Herranz, Julio Martín, Jose M, Fiandor, Michael P. Barrett, Raquel Gabarro, and Coral Barbas ACS Chem. Biol., Just Accepted Manuscript • DOI: 10.1021/acschembio.8b00204 • Publication Date (Web): 19 Apr 2018 Downloaded from http://pubs.acs.org on April 19, 2018

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ACS Chemical Biology

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Metabolic clustering analysis as a strategy for compound selection in the drug discovery pipeline

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for leishmaniasis

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Emily G Armitage1,2,3, Joanna Godzien1, Imanol Peña2, Ángeles López-Gonzálvez1, Santiago Angulo1,

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Ana Gradillas1, Vanesa Alonso-Herranz1, Julio Martín2, Jose M Fiandor2, Michael P. Barrett3, Raquel

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Gabarro2 and Coral Barbas1

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1. Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San

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Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain

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2. GSK I+D Diseases of the Developing World (DDW), Parque Tecnológico de Madrid, Calle de Severo

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Ochoa 2, 28760 Tres Cantos, Madrid, Spain

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3. Wellcome Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation,

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College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK &

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Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical Veterinary & Life

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Sciences, University of Glasgow, Glasgow G61 1QH, UK

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Corresponding author: Coral Barbas. Telephone - +34 913724711; email - [email protected]; postal

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address - Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad

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CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain.

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Abstract

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A lack of viable hits, increasing resistance and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimise prioritisation and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis - mass spectrometry was applied to identify the metabolic profile of Leishmania donovani and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritisation. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritisation of these compounds for the development of new anti-leishmanial drugs.

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Key words: Drug discovery; drug repurposing; metabolomics; principal components analysis;

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Leishmania donovani; mode of action

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Abbreviations: MoA – mode of action; CE-MS – capillary electrophoresis – mass spectrometry; MSI –

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metabolomics standards initiative; QC – quality control; PCA principal components analysis; gas

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chromatography – mass spectrometry (GC-MS); liquid chromatography – mass spectrometry (LC-MS)

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Authors declare no conflict of interest

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Due to a lack of viable hits or increasing resistance to currently available treatments, the bottleneck

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in research towards new therapies for many different diseases is a growing concern. Limited

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knowledge on the mode of action (MoA) or polypharmacological effects of existing treatments could

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be hindering the discovery of new compounds. Studying compound MoA is valuable to understand

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how they could be improved, to propose combination therapies looking for synergistic actions and

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also to determine possible toxic effects. For diseases where drug repurposing is a popular approach

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e.g. for neglected tropical diseases, MoA studies are specifically important since compounds were

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not originally designed to target the new disease type. Un-targeted approaches to study MoA are

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useful when compounds are suspected to have polypharmacy effects beyond known targets and to

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cluster compounds with the same MoA for improving the selection for further in vivo studies.

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Metabolomics offers a valuable approach to clustering compounds on MoA1.

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Neglected tropical diseases are a prime example where resistance to current treatments is

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problematic, funding is limited and drug repurposing is popular. There is a requirement for novel

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strategies in the drug discovery pipeline for medium throughput screening that combines a balance

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on breadth and depth of knowledge on drug MoA. The leishmaniases are a spectrum of neglected

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tropical diseases caused by protozoa of the genus Leishmania. Leishmania donovani provokes one of

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the most severe forms that is visceral leishmaniasis2 and existing therapeutic options for this are

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limited3. From a recent screening of 1.8 million compounds against the three kinetoplastid parasites

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most relevant to human disease (Leishmania donovani, Trypanosoma brucei and Trypanosoma

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cruzi), 192 non-cytotoxic active hits against Leishmania donovani were selected to be included in the

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so called Leishmania box2. While some general hypotheses were generated relating to compound

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structure, suggesting that many of them could target kinases, proteases, cytochromes and host-

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pathogen interactions, the MoA of each is still unknown. Classification into MoA is an important

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element in analysing activity data. To optimise prioritisation and accelerate discovery, a strategy to

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cluster compounds based on more than chemical structure is required. Metabolomics and other hit-

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to-screen assays can be powerful tools in the analysis of MoA4.

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The metabolomics approach to study the MoA of compounds for drug discovery purposes has been

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successfully applied in many fields. For recent reviews, see Vincent and Barrett 20155 for

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parasitology; Armitage and Southam et al. 20166 for oncology; Rankin et al. 20167 for cardiology;

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Adamski 20168 for diabetes; Atzori et al. 20129 for perinatology;

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osteoporosis drug discovery; dos Santos et al. 201611 for antibacterial MoA of plant derived

Introduction


Gennari et al. 201510 for


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products; Mikami et al. 201212 for updates specific to MS-based metabolomics and Hoerr et al.

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201613 for updates specific to NMR-based metabolomics.

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Studying compound MoA can be challenging, especially since it is difficult to distinguish drug effects

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from generic stress responses. A way to overcome this is to study many compounds in the same

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organism in parallel so that generic stress responses can be identified in all drug treated samples

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(therefore not drug specific). Different approaches can be taken to study MoA and the ‘omic’

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approaches can be particularly attractive due to their medium-high-throughput screening

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capabilities combined with high sensitivity and coverage. Moreover, integration of omic data with in

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silico network analysis is a systems pharmacology approach that can be used to identify compound

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MOA on a multiscale14.

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The metabolomics approach has recently been applied to study compounds of the Malaria box,

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another tropical disease with similar unmet medical needs15,16. Metabolomics screening was applied

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to reveal the metabolic perturbations induced by 90 of the almost 30,000 compounds that were

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previously shown to selectively inhibit growth of cultured P. falciparum asexual red blood cell stages,

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in addition to samples treated with known anti-malarials. The key features of the medium-high

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throughput screen were the use of the 96-well format and the use of high-sensitivity LC-MS to

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reproducibly detect 460 putatively annotated metabolites from a range of metabolic pathways.

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Though the number of compounds and the number of metabolites detected was high, authors of

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this study reported significant batch effects using this experimental design that were partially

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overcome by normalisation of treated samples to untreated controls on each plate, but systematic

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variation was still observed in a subset of the drug treatments. Moreover, single doses of 1µM were

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studied for 5h of exposure, irrespective of growth inhibition rates, meaning that some treatments

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did not elicit metabolic response under the conditions tested.

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The Leishmania box contains a total of 192 compounds2. In the present research, lead compounds of

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the Leishmania box have been screened using metabolomics. . Twenty-eight compounds (27

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compounds or analogues from the box in addition to amphotericin B) have been studied in

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Leishmania donovani axenic amastigotes, chosen as the most relevant in vitro model of human

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leishmaniasis. Samples of parasites exposed to each compound were prepared in parallel with un-

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treated control samples and analysed using an un-targeted metabolomics approach to reveal the

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similarities and differences in the metabolome following treatment. The dose of compound and

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exposure time was chosen based on individual kill kinetics[1, 14].


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We aimed to reveal clusters of compounds with similar action against the parasite metabolome, as

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shown previously for anti-malarials15. The identification of clusters allows selection of compounds

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for further consideration in the drug discovery pipeline. Capillary electrophoresis – mass

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spectrometry (CE-MS) was chosen as the analytical tool to study the metabolomes of treated

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parasites, combined with definitive identification of the majority of the metabolic profile screened

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(metabolomics standards initiative (MSI) level 118). Moreover, due to the scale of the study, analyses

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were performed in batches and data were integrated for processing. As one of the largest scale

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metabolomics studies employing CE-MS, important strategies were identified in data treatment that

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build on previously observed limitations in metabolomics and could be useful in the field beyond the

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scope of this research.

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Results and discussion

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2.1

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Following filtration to remove features directly associated with specific compounds and therefore

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likely metabolites of the compounds themselves, in addition to filtration by QC RSD (keeping those

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features with RSD < 30%), 174 features remained and data were assessed for quality and batch

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effect. Supplementary figure 1S shows an overview of the analyses from three analytical batches

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considering the internal standard signal, total useful signal and total number of features. As shown,

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certain samples had particularly low numbers of features and total useful signal. These samples

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corresponded to five specific compound groups in addition to one anomalous sample from another

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group. Parasite numbers calculated for each sample before and after washing were consulted to

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confirm that these lower profiles did not occur because of lower parasite number in those samples.

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Trends in signal were observed in the internal standard and total useful signal. Three methods of

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normalisation were performed to observe how data quality could be improved to remove this batch

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effect. Supplementary figure 2S shows the scores plots generated for the first two PCs before and

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after normalisation by total useful signal, internal standard and a commonly used method in

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metabolomics – locally estimated scatterplot smoothing (LOESS). Normalisation by internal standard

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was deemed most appropriate since it did not skew the remaining samples based on the number of

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features present as total useful signal normalisation did.

Assessment of data quality and overview of entire analysis

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Batch effects are common and often unavoidable in large-scale studies19,20. The challenge has been

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addressed previously, mainly for gas chromatography – mass spectrometry (GC-MS) and liquid

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chromatography – mass spectrometry (LC-MS) data21,22, although to our knowledge has not been

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addressed for CE-MS based metabolomics. Moreover, CE-MS is often discounted based on its

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reputation for irreproducibility, particularly in migration time, although advancements in technology

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and methodology are making CE-MS increasingly popular23. In our experience, careful choice of

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analytical method, experimental design and studying of raw data to find the best parameters for

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alignment of analytical batches, makes CE-MS a robust and viable choice in multiple-batch studies,

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especially for ionic and polar metabolites where the alternative mechanism would be to use HILIC

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based LC-MS, that has a deeper complexity of issues surrounding robustness and reproducibility24.

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2.2

Identification of Leishmania donovani axenic amastigote metabolic profile

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Before further multivariate analysis, identification of the entire CE-MS profile was performed for un-

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treated parasite samples. The 174 peaks following filtration and normalisation were first annotated

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and from this 105 were found to be unique features. The remaining features were identified as

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fragments, dimers or artefacts of other metabolites present in the data and were therefore removed

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in all further multivariate analysis. All features passed filters for presence in un-treated parasites and

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as such this identified profile serves as the first complete metabolic profile of Leishmania donovani

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axenic amastigotes in CE-MS. A total of 36 metabolites could be definitively identified to MSI level 1,

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determined through analysis of authentic standards and a further 10 were identified to level 2. A

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network showing metabolic interactions is shown in Figure 2, where MSI level 1 identified

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metabolites are highlighted in bold. KEGG enzyme numbers are shown. Yellow dots indicate

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metabolites closely relating detected metabolites but that were not detected themselves.

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Supplementary table 2 details the experimental m/z, migration time and MSI level of identification

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for all 105 uniquely distinguished metabolites.

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2.3

Metabolic clustering analysis by principal components

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Multivariate analysis of data using PCA can be challenging, especially when the experimental and

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biological complexity increases. For example, in the related study of metabolomics on malaria box

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compounds, the first two PCs revealed only stochastic biological/experimental variation, while

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usable information was embedded in PC3 onwards15, representing a low proportion of total

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variability in the model. To explore this in our data, two approaches of metabolic clustering were 6 ACS Paragon Plus Environment

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employed to study the similarities and differences in parasites treated with one compound

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compared with un-treated cells. These approaches together revealed complementary information

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on the likely MoA of different compound clusters that could be used to select a subset of

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compounds for further analysis in the drug discovery pipeline for leishmaniasis. In both cases, all

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features (identified, annotated or unidentified, as detailed in supplementary table 2) were included

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in the multivariate analyses. In the first approach, a further filter to keep only those features with

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RSD

Metabolic Clustering Analysis as a Strategy for Compound Selection

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