Chemical genetics of Plasmodium falciparum

Abstract

Malaria caused by Plasmodium falciparum is a disease that is responsible for 880,000 deaths per year worldwide. Vaccine development has proved difficult and resistance has emerged for most antimalarial drugs. To discover new antimalarial chemotypes, we have used a phenotypic forward chemical genetic approach to assay 309,474 chemicals. Here we disclose structures and biological activity of the entire library-many of which showed potent in vitro activity against drug-resistant P. falciparum strains-and detailed profiling of 172 representative candidates. A reverse chemical genetic study identified 19 new inhibitors of 4 validated drug targets and 15 novel binders among 61 malarial proteins. Phylochemogenetic profiling in several organisms revealed similarities between Toxoplasma gondii and mammalian cell lines and dissimilarities between P. falciparum and related protozoans. One exemplar compound displayed efficacy in a murine model. Our findings provide the scientific community with new starting points for malaria drug discovery.

Figure 1. Chemical structure network graph and antimalarial potencies of the 1300 validated hits

Topologically similar molecules cluster together in the branches of the network. To construct the graph, molecules were first abstracted to scaffolds and then further to cores using the Murcko algorithm. Each of these structural entities is represented as a node, and nodes are connected via edges according to topological relationships. Molecular nodes are coded to reflect potency against PfK1 (low = white, high = blue) and Pf3D7 (low = small, high = large). The highly branched structure of the full network graph (bottom half of the figure) indicates that the 1300 compounds are organized into clusters of clusters: cores are well sampled by multiple scaffolds, and the cores themselves are grouped into families of related chemotypes. Previously reported antimalarial compounds are highlighted. The top half of the figure provides greater detail on three potent chemotypes with well-developed structure activity relationships: (A) tetrahydroisoquinoline, (B) naphthoquinone, and (C) dihydropyridine. Data in supplementary file (‘Guiguemde_Excel_SI’, ‘Structure’ tab).

Figure 2. Reduced representation of the network map showing synergistic activities with clinically relevant antimalarials

The size of the nodes reflect the magnitude of the logarithmic difference between EC50 in the presence and absence of EC10 of exemplar antimalarial drugs. Absolute differences less than one log unit were not considered significant. Synergistic and antagonistic compounds are uniformly color-coded blue and red, respectively. Highly synergistic compounds can be seen with artemisinin and mefloquine. Data in supplementary file (‘Guiguemde_Excel_SI’, ‘Synergy’ tab).

Figure 3. Reduced representation of the network map showing the interaction of the cross-validated hits with potential biological targets

The network map on the left displays compounds targeting well validated protein targets as measured in inhibition assays (EC₅₀ ≤ 15 μM). The map on the right shows compounds that bind to purified malarial proteins according to thermal-melt shift experiments. The size of nodes representing active or binding compounds is increased for clarity. Note that few of the compounds discovered in this study are potent inhibitors of known targets or bind potently to putative targets. Data in supplementary file (‘Guiguemde_Excel_SI ’, ‘Sensitivity’ tab).

Figure 4. Phylochemogenetic profiling

Phylochemogenetic analysis of the cross-validated compounds using a two-way hierarchical clustering. of growth inhibition against P. falciparum strains (Pf3D7, PfK1, PfV1/S), other eukaryotic parasites (Toxoplasma gondii [Tg], Trypanosoma brucei [Tb], Leishmania major [Lm]), and human cell lines (HEK293, BJ, Raji, and HepG2). Columns represent single compounds and are clustered according to potency against the cell lines and organisms. Neighboring compounds share a similar potency spectrum. Rows represent a single cell line or organism and are clustered according to their chemosensitivity to the compounds in the study. A phylogenetic tree of the organisms in this study is provided for reference. Note that despite the many known examples of taxonomically-conserved pathways, on a global level, phylogeny is a poor predictor of chemical sensitivity profiles: Toxoplasma responses more closely parallel human than their evolutionary siblings, Plasmodium. Data in supplementary file (‘Guiguemde_Excel_SI’, ‘Phylo’ tab).