Malaria mortality has decreased substantially since 2000, largely due to the success of artemisinin combination therapies (ACTs). However, these gains are threatened by the emergence of artemisinin resistance in Southeast Asia. If artemisinin resistance spreads from Southeast Asia to Africa, as has happened for other drugs, malaria deaths due to Plasmodium falciparum would increase dramatically. There is an urgent need to develop new classes of effective antimalarial drugs and to extend the effectiveness of ACTs.
Historically, ACTs contain partner drugs with a long metabolic half-life to counteract the rapid metabolism of artemisinin. Ideally, partner drugs in combination therapies should act synergistically, targeting different pathways. This requires knowledge of drug mechanism of action (MoA) and mechanism of resistance (MoR), which may not be related to MoA. Unfortunately, MoA and MoR are difficult to determine for large numbers of drugs using standard methods. Understanding MoA and MoR for artemisinin and candidate drugs will enhance our ability to rationally identify optimal ACT partner drugs to overcome existing resistance and to buy time to allow new drugs to progress through the drug development pipeline.
Transcription profiling and chemogenomic profiling are ideal for quickly evaluating MoA of many compounds. Chemogenomic profiling is a powerful tool for determining drug MoA by comparing profiles of susceptibilities to a panel of drugs in a collection of mutant lines representing a wide range of disrupted genes. In our studies, artemisinin functional activity was linked to signal transduction and cell cycle regulation pathways. Transcription profiling of Plasmodium parasites perturbed briefly with sub-lethal concentrations of drug can be used to generate a drug pathway fingerprint to infer MoA. Transcriptional signatures of artemisinin resistance can be used to predict novel partner drugs for ACTs to treat artemisinin resistant infections.
Artemisinin MoR is not fully understood. Pathway-based networks analysis of genomic variation data and gene expression data from published studies of artemisinin resistance can provide a more complete understanding of the mechanism underlying artemisinin resistance. Understanding MoA for drug candidates, along with a thorough understanding of artemisinin MoR would enhance efforts to design new combination therapies that can circumvent artemisinin resistance.