Benefits of using multiple first-line therapies against malaria

Abstract

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.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Schematic of basic model dynamics; see SI Appendix for full equations. The model allows for multiple types of resistant strains to be circulating in the population; for simplicity, the model diagram shows the dynamics for only one strain. Susceptible individuals (S) can become asymptomatic (A) or symptomatic/clinical (C) after receiving an infectious bite from a mosquito. Unsuccessful treatment results from drug resistance; successful treatment occurs when the infecting parasites do not have resistance to the drug(s) being used. When hosts in the C classes are undergoing treatment, parasites can evolve de novo resistance to the drugs being used (not shown on diagram). The curved arrow indicates that hosts can acquire immunity as a result of clinical disease.

Fig. 2.

Differences between single and multiple first-line therapies. Here, R0 = 3, si = 0.1, σi = 10−5, f = 0.6, and F = 1.0. System is started at its endemic equilibrium, and treatment is begun at time 0. (Top) Percentage of hosts undergoing a clinical episode of malaria (red line) and percentage of all hosts that are infected (black line); red axis labels correspond to red line. (Middle) Frequency of single- (thin black line), double- (medium black line), and triple-resistant strains (thick black line); a thicker line indicates more resistance. The magenta lines in the second row indicate the fraction of incident treated cases that receive a failing treatment. (Bottom) ϕ the fraction of infections that are currently in a clinical state and possibly being treated by drugs. As in a classic resistance epidemic, a quick initial decrease in disease prevalence and clinical cases is followed by a period of low prevalence, which is followed by a period when prevalence creeps back up almost to pretreatment levels; disease prevalence will not attain its full pretreatment level as long as there is some cost to resistance. The major difference among the treatment strategies is the pattern of fixation in the middle row. Here and in the model in general, resistance evolution begins later and occurs more slowly when more first-line therapies are used.

Fig. 3.

In these graphs, si = 0.1, σi = 10−5, F = 1.0, and the model was run for 20 years; f = 0.6 for the solid lines and f = 0.0 for the dashed lines. Left shows the clinical outcomes DPC (black lines) and NTF (red line) as a function of R0. Right shows the time to emergence (T.05) and time to fixation (T.95) as a function of R0; the dashed red line shows the equilibrium value of ϕ when there is no treatment or resistance. Red lines correspond to red axis labels. For higher R0, ϕ is lower; thus, there is less selection pressure favoring the resistants and their time to emergence/fixation is longer. This relationship breaks down for very low R0 where the parasites' generation time is long and evolution is slow. The inner axis labels correspond to NTF (red numbers) and years until resistance reaches 5% or 95% (black numbers).

Fig. 4.

In these graphs, R0 = 3, σi = 10−5, F = 1.0, and the model was run for 20 years. The black line corresponds to MFT with three drugs, the medium gray line to MFT with two drugs, and the light gray line to a single first-line therapy. Note that using more therapies has a bigger advantage when the cost of resistance is higher. Fig. S2 in SI Appendix shows the bottom row without discounting.

Fig. 5.

Differences between MFT and cycling strategies; “adaptive cycling” means switching drugs at 10% treatment failure with a 1-year switch delay. Here, R0 = 3, si = 0.1, σi = 10−5, f = 0.6, and F = 1.0. Top is as in Fig. 2. Middle tracks the total level of resistance in the parasite population (triple resistants count as fully resistant, double resistants count as 2/3 resistant, and so on); light gray lines show the “total resistance” line from the other two columns for comparison. Bottom tracks the parasites population's mean fitness, calculated in the absence of drug treatment, with a fitness of one assigned to drug-sensitive parasites; the light gray lines show the mean-fitness line from the other two columns for comparison.

Fig. 6.

Results of 1,000 simulations, with R0 = 3, σi = 10−5, f = 0.6, si = 0.1, and F = 1.0, where the drug distribution of three drugs was chosen randomly. Certain drug distributions are highlighted in Upper. Lower shows the same simulations plotted against the frequency of the most used drug. Drug diversity is measured as − pi Σ log pi, where pi is frequency of use of drug i. The 60/20/20 strategy has the same diversity measure as the 45/45/10 strategy, but resistance arrives sooner under the former because one drug has such a high frequency of use.