In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen

Abstract

The growing resistance to current first-line antimalarial drugs represents a major health challenge. To facilitate the discovery of new antimalarials, we have implemented an efficient and robust high-throughput cell-based screen (1,536-well format) based on proliferation of Plasmodium falciparum (Pf) in erythrocytes. From a screen of approximately 1.7 million compounds, we identified a diverse collection of approximately 6,000 small molecules comprised of >530 distinct scaffolds, all of which show potent antimalarial activity (<1.25 microM). Most known antimalarials were identified in this screen, thus validating our approach. In addition, we identified many novel chemical scaffolds, which likely act through both known and novel pathways. We further show that in some cases the mechanism of action of these antimalarials can be determined by in silico compound activity profiling. This method uses large datasets from unrelated cellular and biochemical screens and the guilt-by-association principle to predict which cellular pathway and/or protein target is being inhibited by select compounds. In addition, the screening method has the potential to provide the malaria community with many new starting points for the development of biological probes and drugs with novel antiparasitic activities.

Fig. 1.

Validation of the antimalarial HTS assay. (A) Test results from a titration experiment. A 1:2 dilution of a parasite culture of Pf strain 3D7 starting out at a 5% parasitemia and 2.5% hematocrit was performed with screening media. The trend line was fitted with an R² of 0.997 from a 0.078% to 5% parasitemia (logarithmically scaled in both dimensions). (B) Fluorescence intensity across an entire assay plate. Columns 45 and 46 were treated with 12.5 μM mefloquine, and columns 47 and 48 were treated with 0.125% DMS0. (C) 3D7 dose–response plot with antimalarial control compounds. The EC₅₀ values of artemisinin, mefloquine, pyrimethamine, and primaquine are 9.0 nM, 9.5 nM, 7.2 nM, and 548 nM, respectively.

Fig. 2.

Chemotype and MOA distributions of validated compounds. Chemotypes distinct from any known drugs were found in 95% of the validated hits. Known chemotypes are further categorized based on their MOA assignments from MeSH.

Fig. 3.

Several statistically significant MOA and SAR clusters. (A) Cluster of 30 compounds containing 11 annotated protein synthesis inhibitors. (B) Cluster containing 26 compounds, among which 15 are antimalarials known to be folic acid antagonists. (C) SAR-driven cluster containing 44 staurosporine-like structures sharing the same profile.

Fig. 4.

Predicted mode of binding of the uncharacterized inhibitors to wild-type PfDHFR. Blind docking studies between the diaminopyrimidine class of inhibitors to the crystal structure of PfDHFR revealed a clustering of hits to the active site that shared the same general mode of binding. (A) For comparison, top hits for each of the 11 uncharacterized diaminopyrimidine class of inhibitors superimposed. (B) Top docking result for compound 5 (yellow) shown superimposed to the crystallized complex between NADPH (gray), the antifolate inhibitor WR99210 (red), and PfDHFR (green).