Friday, July 13, 2012

Superinfection and the evolution of resistance to antimalarial drugs

http://www.ncbi.nlm.nih.gov/pubmed/22787024


 2012 Jul 11. [Epub ahead of print]

Superinfection and the evolution of resistance to antimalarial drugs.

Source

Department of Ecology and Evolutionary Biology, Princeton University, , Princeton, NJ 08544, USA, Princeton Environmental Institute, Princeton University, , Princeton, NJ 08544, USA, Center for Disease Dynamics Economics and Policy, Washington, DC 20036, USA, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, , Baltimore, MD 21205, USA, Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, , Baltimore, MD 21205, USA.

Abstract

A major issue in the control of malaria is the evolution of drug resistance. Ecological theory has demonstrated that pathogen superinfection and the resulting within-host competition influences the evolution of specific traits. Individuals infected with Plasmodium falciparum are consistently infected by multiple parasites; however, while this probably alters the dynamics of resistance evolution, there are few robust mathematical models examining this issue. We developed a general theory for modelling the evolution of resistance with host superinfection and examine: (i) the effect of transmission intensity on the rate of resistance evolution; (ii) the importance of different biological costs of resistance; and (iii) the best measure of the frequency of resistance. We find that within-host competition retards the ability and slows the rate at which drug-resistant parasites invade, particularly as the transmission rate increases. We also find that biological costs of resistance that reduce transmission are less important than reductions in the duration of drug-resistant infections. Lastly, we find that random sampling of the population for resistant parasites is likely to significantly underestimate the frequency of resistance. Considering superinfection in mathematical models of antimalarial drug resistance may thus be important for generating accurate predictions of interventions to contain resistance.

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