Malaria remains one of the world's great global health problems, still responsible for widespread morbidity and mortality, and especially among children under 5-years-old. Alarmingly, the specter of widespread resistance to antimalarial drugs threatens to reverse recent progress made in the public health sector, potentially rendering our available therapeutic options ineffective. To address this clear and present threat, the biomedical community needs to identify more effective ways of treating malaria that undermine or prevent the evolution of drug resistance. In addition, the malaria community needs new perspectives on drug resistance that might foster entirely new directions for therapy. The proposed project is engineered to test the hypothesis that a greater mechanistic understanding of antimalarial resistance can lead to the creation of better predictive models for the evolution of drug resistance, which could aid in our quest to address the antimalarial resistance problem. This hypothesis is based on two sets of recent findings: (1) recent studies have revealed the biophysical and biochemical mechanism of drug resistance in bacterial pathogens. (2) Improvements in computational evolution allow for a more refined picture of evolution across genotype-phenotype maps.
Two specific aims are proposed to test the larger hypothesis.
AIM 1, will create genotype-phenotype maps for dihydrofolate reductase (DHFR, an enzyme target of several antimicrobial drugs malaria and other pathogens) resistance to several antifolate drugs. In addition, AIM 1 will measure biochemical and biophysical properties of the mutations that compose the genotype-phenotype map for resistance.
AIM 2, will construct computational predictive models of evolution, and test several theoretical antimalarial treatment strategies in silico. In sum, these AIMS offer multiple methods?experimental, mathematical, and computational?that will foster a richer picture of antimalarial evolution. This picture would contribute to the longer-term goal of being able to undermine the evolution of antifolate resistance through more responsible use of existing drugs, and identify of unexplored targets for new drugs.
