Ahead of the curve: next generation estimators of drug resistance in malaria infections
Department of Medicine, Division of Transfusion Medicine; Program in Bioinformatics and Integrative Biology
Medical Subject Headings
Animals; Antimalarials; Drug Monitoring; *Drug Resistance; High-Throughput Nucleotide Sequencing; Humans; Malaria; Phenotype; Plasmodium falciparum; Sequence Analysis, DNA
Bioinformatics | Computational Biology | Immunology of Infectious Disease | Parasitic Diseases | Parasitology
Drug resistance is a major obstacle to controlling infectious diseases. A key challenge is detecting the early signs of drug resistance when little is known about its genetic basis. Focusing on malaria parasites, we propose a way to do this. Newly developing or low level resistance at low frequency in patients can be detected through a phenotypic signature: individual parasite variants clearing more slowly following drug treatment. Harnessing the abundance and resolution of deep sequencing data, our 'selection differential' approach addresses some limitations of extant methods of resistance detection, should allow for the earliest detection of resistance in malaria or other multi-clone infections, and has the power to uncover the true scale of the drug resistance problem.
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Citation: Mideo N, Kennedy DA, Carlton JM, Bailey JA, Juliano JJ, Read AF. Ahead of the curve: next generation estimators of drug resistance in malaria infections. Trends Parasitol. 2013 Jul;29(7):321-8. doi: 10.1016/j.pt.2013.05.004. Link to article on publisher's site