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

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.