Global phenotypic screening for antimalarials

Abstract

Malaria, a devastating infectious disease caused by Plasmodium spp., leads to roughly 655,000 deaths per year, mostly of African children. To compound the problem, drug resistance has emerged to all classical antimalarials and may be emerging for artemisinin-based combination therapies. To address the need for new antimalarials with novel mechanisms, several groups carried out phenotypic screening campaigns to identify compounds inhibiting growth of the blood stages of Plasmodium falciparum. In this review, we describe the characterization of these compounds, explore currently ongoing strategies to develop lead molecules, and endorse the concept of a "malaria box" of publicly accessible active compounds.

Figure 1. Physiochemical Characteristics of Validated Antimalarial Hits

Linear discriminant analysis of the physiochemical characteristics of validated high-throughput screening hits compared with FDA-approved small molecule drugs (oral and parenteral), commercially available HTS compounds, known natural products, and commercially available fragments. Shading represents the density of the distribution of approved drugs within the space. Lines represent the perimeter of the space containing 90% of the described compound collections. The composition of each LDA ordinate is described by the list of the physiochemical properties, sorted in order of decreasing magnitude and colored according to sign (red = negative contribution; blue = positive contribution). Approved antimalarial drugs are depicted by open circles, with structures. The potent subset of each hit set is rescricted to compounds with EC50 < 100 nM. Despite all three libraries being designed to overlay the centroid of the drug space, each screen selected compounds that were significantly shifted away from this centroid.

Figure 2. Scaffolds Observed in Validated Antimalarial Hits

A network graph showing the distribution of scaffolds within the hit sets in comparison with one another. Scaffolds were defined from molecules using the Schuffenhauer fragmentation algorithm(Schuffenhauer, et al., 2007; Schuffenhauer, et al., 2007), but requiring at least two rings unless the molecule as acyclic or possessed only one ring. Scaffold nodes were arranged into parent-child hierarchies based on substructure relationships. The resulting root scaffolds were clustered using Tanimoto distance (FCFP_4 fingerprint) to generate 399 scaffold families. Each family was then assigned a node. Parent-child scaffold substructure relationships and cluster membership define graph edges. Scaffold nodes are colored by library membership, sized by number of molecules derived from the scaffold, and shaped according to whether or not at least one molecule from the scaffold is potent (EC50 < 100 nM). Structures of the root scaffolds from the ten largest scaffold families are shown. Each group independently identified most scaffold families, although the coverage of each scaffold by analogues present in individual hit sets was highly variable. As a result, often only one library contained the most potent member of a given scaffold. This may have driven the divergence in “high priority” scaffolds among the group.

Figure 3. Phenotypic Drug Discovery Strategy for Malaria

The phenotypic strategy utilizes early deployment of cellular and pharmacologic constraints on molecule selection, leading rapidly to compounds with a high probability of activity in vivo. This pushes target identification and validation to later in the process, off the critical path. However, understanding the mechanism of action and acquisition of resistance are related.

Figure 4. The Malaria Box Concept

The malaria box is a single clearinghouse that would warehouse data, models, and physical compounds and serve as a hub for collaboration. Community members would release constituents to the box, which would then be an open source to facilitate further work on parasitology, chemistry, pharmacology, and model building. To access the contents of the box, collaborators would need to agree to participate in the open source model.