Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis

Abstract

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization 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/prioritization. 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 prioritization of these compounds for the development of new antileishmanial drugs.

In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages

“…Another aspect that should be taken into consideration is metabolic changes induced in host cells by Leishmania parasites. While we recognize that metabolomic analysis represents an important aspect that has recently been explored in the field of leishmaniasis (Armitage et al, 2018 ; Cuypers et al, 2018 ), which certainly contributes to the understanding of disease, the results from these studies fall outside the scope of the present study. The present review instead focuses on global transcriptome analysis of macrophages in response to infection, which has been poorly investigated using RNA-seq technology (Dillon et al, 2015 ; Fernandes et al, 2016 ).…”

Experimental Strategies to Explore Drug Action and Resistance in Kinetoplastid Parasites

“…The primary challenge of the technique lays in the data deconvolution and in the identification of metabolites, as the kinetoplastid metabolomes have not yet fully been elucidated [ 215 ]. Even so, new pathways can be discovered, and compounds can still be classified based on their metabolic fingerprint [ 222 , 223 , 224 ].…”

A Non-Targeted Capillary Electrophoresis-Mass Spectrometry Strategy to Study Metabolic Differences in an In Vitro Model of High-Glucose Induced Changes in Human Proximal Tubular HK-2 Cells

“…Methionine sulfone was chosen over tyramine because the QC samples were better clustered together when using IS as a normalization peak, which is shown in Figure S3. In metabolomics studies by CE-MS, correction to IS is common because of larger variability inherent to this technique [32,33]. Data were further normalized to the protein content as in previous contributions [7,34,35].…”