Benefits of using multiple first-line therapies against malaria.

Journal article

Despite the availability of many drugs and therapies to treat malaria, many countries' national policies recommend using a single first-line therapy for most clinical malaria cases. To assess whether this is the best strategy for the population as a whole, we designed an evolutionary-epidemiological modeling framework for malaria and compared the benefits of different treatment strategies in the context of resistance evolution. Our results show that the population-wide use of multiple first-line therapies (MFT) against malaria yields a better clinical outcome than using a single therapy or a cycling strategy where therapies are rotated, either on a fixed cycling schedule or when resistance levels or treatment failure become too high. MFT strategies also delay the emergence and slow the fixation of resistant strains (phenotypes), and they allow a larger fraction of the population to be treated without trading off future treatment of cases that may be untreatable because of high resistance levels. Earlier papers have noted that cycling strategies have the disadvantage of creating a less temporally variable environment than MFT strategies, making resistance evolution easier for the parasite. Here, we illustrate a second feature of parasite ecology that impairs the performance of cycling policies, namely, that cycling policies degrade the mean fitness of the parasite population more quickly than MFT policies, making it easier for new resistant types to invade and spread. The clinical benefits of using multiple first-line therapies against malaria suggest that MFT policies should play a key role in malaria elimination and control programs.

Authors: Boni, M; Smith, D; Laxminarayan, R

• Journal: Proceedings of the National Academy of Sciences of the United States of America
• Volume: 105
• Issue: 37
• Pages: 14216-14221
• Publication date: 2008-09-01
• DOI: 10.1073/pnas.0804628105
• EISSN: 1091-6490
• ISSN: 0027-8424
• Language: English
• Keywords: Computer Simulation; Humans; Models, Biological; Antimalarials; Malaria; Time Factors