JAC Advance Access originally published online on April 26, 2006
Journal of Antimicrobial Chemotherapy 2006 58(1):47-51; doi:10.1093/jac/dkl158
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Plasmodium falciparum infection dynamics and transmission potential following treatment with sulfadoxine-pyrimethamine
1 Australian Centre for International and Tropical Health and Nutrition, University of Queensland Brisbane, Australia 2 Malaria Drug Resistance and Chemotherapy Laboratory, Queensland Institute of Medical Research Brisbane, Australia 3 Department of Drug Resistance and Diagnostics, Australian Army Malaria Institute Brisbane, Australia
*Correspondence address. Malaria Drug Resistance and Chemotherapy Laboratory, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Queensland 4029, Australia. Tel: +61-7-3362-0416; Fax: +61-7-3362-0104; E-mail: michelle.gatton{at}qimr.edu.au
Received 18 July 2005; returned 27 March 2006; revised 29 March 2006; accepted 3 April 2006
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Abstract |
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Objectives: To investigate the overall efficacy of sulfadoxine-pyrimethamine (SP) treatment and the corresponding transmission potential for patients infected with SP-resistant Plasmodium falciparum.
Methods: A mathematical model of the in-host dynamics of P. falciparum infections was used to simulate infections with parasites having different numbers of mutations in the dhfr and dhps genes and their responses to SP treatment. The treatment outcome and transmission potential of each simulated infection following SP treatment was assessed by tracking asexual parasite density and the number of days with sufficient mature gametocytes to give a >95% probability of infecting a mosquito.
Results: The results show treatment failure only occurring in patients infected with parasites having two mutations in dihydrofolate reductase (DHFR) combined with at least two mutations in dihydropteroate synthetase (DHPS) or with parasites having a triple mutation in DHFR. Highly mutated parasites (three mutations in each gene) caused the highest clinical failure rate, while moderately mutated parasites (three mutations in DHFR plus one mutation in DHPS) produced a high rate of asymptomatic parasitological failure following SP treatment. This high rate of asymptomatic recrudescence caused the transmission potential of infections with moderately resistant parasites to exceed that of highly resistant parasites.
Conclusions: The model output suggests that infection dynamics following SP treatment and the overall transmission potential are inherently linked. The combination of prolonged asymptomatic parasitaemia and increased transmission potential allows parasites having three mutations in DHFR, but fewer mutations in DHPS, to expand largely unnoticed.
Keywords: malaria , drug resistance , mathematical model
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Introduction |
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Sulfadoxine-pyrimethamine (SP) is the first-line treatment for Plasmodium falciparum infections in many countries; however, the clinical efficacy of the combination is decreasing due to rapid development of resistance within P. falciparum parasite populations. Pyrimethamine and sulfadoxine act synergically to inhibit two enzymes important in the parasite's folate biosynthetic pathway, dihydrofolate reductase (DHFR) and dihydropteroate synthetase (DHPS).1 The point mutations S108N, N51I, C59R and I164L in the dhfr gene have been shown to confer resistance to pyrimethamine.2,3 Likewise, point mutations at positions 436, 437, 540 and 613 in the dhps gene confer resistance to sulfadoxine.3–5 Decreasing in vitro P. falciparum susceptibility and in vivo treatment failure are related to the number of mutations in each gene,2,3,5–9 and it has been reported that the presence of the quintuple mutant (triple DHFR mutant at positions 108, 51 and 59, and DHPS double mutant at positions 437 and 540) is strongly associated with SP treatment failure.10,11 The evolution of SP resistance has only become clear due to recent work demonstrating that SP-resistant P. falciparum parasites carrying the triple mutation within south-east Asia and SP-resistant P. falciparum with triple mutations in Africa share a single origin, thus indicating a strong selective sweep across the continents.12,13 The results contradict the general belief that SP resistance developed at multiple sites, since mutations in DHFR occur at relatively high frequencies and mutants can be readily selected in vitro.14 Understanding the sweep process may assist in preventing the spread of resistance to new antimalarial drugs.
The factors associated with selection of SP-resistant parasites have been explored using a variety of mathematical models.15,16 More recently, a model has been developed to estimate the useful therapeutic life of SP, alone and in combination with other drugs such as artesunate and amadiaquine.17 One of the key assumptions of these models relates to the transmissibility of resistant parasites following treatment. It has generally been assumed that the overall transmission potential is directly proportional to the probability of asexual parasites surviving treatment, but this assumption has not been tested.
This study uses a previously developed mathematical model to simulate the overall treatment outcome of patients infected with P. falciparum parasites with different levels of SP resistance and their corresponding transmission potential. The results suggest that moderately resistant parasites have the highest transmission potential due to delays in recrudescences of patent parasitaemia and clinical symptoms caused by the suppressive effect of residual SP, thereby facilitating the spread of SP resistance.
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Materials and methods |
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We have previously reported a computer simulation model mimicking P. falciparum infection dynamics in naive hosts.15 For this study, the model was modified to incorporate gametocyte production. Briefly, the in-host dynamics model was used to simulate the interaction between blood-stage parasites and the human host, with outputs being the parasite density and host response (symptomatic or asymptomatic) after every parasite replication cycle (i.e. 48 h) (Figure 1). Various host and parasite factors were incorporated into the model, including non-specific and specific host immune responses and parasite antigenic variation. The specific equations and values used to represent the various parameters in the model were as previously reported for the El Limon and Santee Cooper isolates of P. falciparum.15,18
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Parasites initiating an infection were classified by the number of mutations in DHFR (0–3) and DHPS (0–3). Treatment with a standard dose of SP (75 mg pyrimethamine plus 1500 mg sulfadoxine) was initiated at the onset of symptoms and treatment effect simulated by reducing the parasite load in accordance with the parasite susceptibility.15 The probability of parasites surviving at various time points following treatment was determined using data from SP isobolograms and dose–response curves, the drug elimination half-life and the time since treatment.15 In the current simulation study treatment with SP was introduced when the non-specific immune response of the host was likely to produce a fever (Figure 1).
Both asexual and gametocyte densities were monitored in the model. The number of gametocytes produced at each replication cycle was calculated by applying a conversion rate, based on a log-normal distribution with mean 0.0032, to the asexual parasitaemia.19 This conversion rate was doubled in response to the development of clinical malaria and increased further following SP treatment (mean increased to 0.0264).19,20
Treatment outcomes were classified based on the parasitological and clinical status of the simulated individual; classifications were treatment success, clinical failure (with symptoms) or parasitological failure (asymptomatic). Re-infection after SP treatment was not considered in the simulations, and hence any individual with parasites at any time more than 4 days after treatment was defined as a treatment failure. It was assumed that all clinical failures were successfully given rescue treatment with a different drug and that this completely resolved the infection.
The transmission potential of each infection was estimated by the number of days with >95% chance of infecting a mosquito with at least one male and female gametocyte. The gametocyte density corresponding to this probability (20.8 infectious gametocytes/µL blood) was calculated assuming (i) a mosquito blood-meal of 1 µL; (ii) the number of gametocytes ingested by a mosquito followed a negative binomial distribution with constant over-dispersion;21 (iii) a male to female gametocyte ratio of 1:4;22 and (iv) that only gametocytes older than 10 days were infectious.23 The lifespan of mature circulating gametocytes was assumed to follow a log-normal distribution with mean 6.4 days,19 and early stage gametocytes were assumed susceptible to host anti-P. falciparum erythrocyte membrane protein 1 (PfEMP1) antibodies and SP treatment.23,24
Infections with parasites having 0, 2 or 3 mutations in DHFR, combined with 0, 1, 2 or 3 mutations in DHPS, were simulated. A total of 100 simulations were conducted for each DHFR/DHPS combination. The in-host dynamics model was programmed in Fortran 95 (Lahey Computer Systems Inc, USA), and the simulation results were analysed using SPSS (Version 13.0, SPSS Inc., USA). Differences in proportions were analysed for statistical significance using the 
2 test, while differences between means were tested using either a t-test (comparison of two means) or an analysis of variance (ANOVA; comparison of >2 means).
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Results and discussion |
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Clinical symptoms and initial treatment occurred 18, 20 and 22 days after infection by mosquito bites, in 49.0% (396/800), 50.1% (401/800) and 0.4% (3/800) of simulations, respectively. The incubation time, which includes the hepatic (assumed to be 6 days) and erythrocytic development, was independent of parasite genotype (P = 0.76, n = 800). Treatment failure was only observed in infections with parasites having two mutations in DHFR combined with at least two mutations in DHPS and with parasites having a triple mutation in DHFR with or without mutations in DHPS (Figure 2).
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For simulations where patients failed SP treatment, the clinical failure rate was dependent on parasite genotype. Patients infected with parasites having triple mutations in both DHFR and DHPS showed the earliest re-presentation of symptoms requiring rescue treatment and the highest rescue treatment rate (P < 0.001, n = 416) (Figure 2). Patients infected with parasites carrying fewer mutations often did not develop clinical symptoms until the second or third parasitaemia peak in the recrudescence, thereby delaying the time to rescue treatment. This delay was the result of decreased net growth/multiplication of less mutated parasites brought about by the residual SP. This residual drug effect often produced sub-patent parasitaemia (<10 parasites/µL) in the weeks following SP treatment, which allowed the development of the host's specific immune response to further control parasitaemia delaying recrudescence of clinical symptoms. This residual drug had little effect on highly mutated parasites; hence, highly mutated parasites tended to recrudesce earlier and were more likely to produce clinical symptoms following treatment.
For simulations where treatment successfully terminated the infection, the transmission potential of the parasites was significantly affected by the time of initial onset of symptoms and treatment and parasite genotype (P = 0.020). When the onset of symptoms and treatment occurred 18 days post-infection, parasites with wild-type or only two mutations in DHFR had significantly fewer days of high transmission potential (mean = 6.1 days, SEM = 0.3 days) than parasites with either 3/0 or 2/3 mutations in DHFR/DHPS (mean = 9.2 days, SEM = 0.3 days) (P < 0.0001) (Table 1). Infections having an initial onset of symptoms and treatment at 20 days post-infection showed no significant differences in transmission potential between DHFR/DHPS allelic types (P = 0.306, mean = 16.7 days, SEM = 0.4 days) (Table 1). The influence of initial disease onset and treatment day on the subsequent transmission potential of an infection is most likely related to the extra 2 days for gametocyte maturation, as opposed to the higher absolute number of gametocytes, since in the model early-stage gametocytes are killed by SP treatment.
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In contrast to the treatment successes, the transmission potential of treatment failures was not influenced by initial disease onset and treatment day, but did differ between parasite genotypes (P < 0.0001). Infections with the most-resistant parasites (three mutations in DHFR and three mutations in DHPS) had the lowest transmission potential (mean = 49.1 days, SEM = 2.1 days) due to the likelihood of early recrudescence and rescue treatment (Table 1). Interestingly, infections with parasites having moderate resistance, such as a triple mutation in DHFR and a single mutation in DHPS, showed the highest transmission potential (mean = 75.9 days, SEM = 3.1 days). This resulted directly from the high probability of asymptomatic recrudescence for this parasite genotype producing sufficient number of gametocytes for transmission (Figure 2).
The sensitivity of the model output to changes in treatment dose, fever threshold and initial number of merozoites released into the bloodstream at the start of the asexual infection were assessed. Fever threshold (parasite density triggering a fever) was the only variable found to significantly affect the reported model output. A lower fever threshold resulted in earlier onset and treatment, and this impacted on both the treatment failure rate and the transmission potential. The earlier treatment time led to a higher rescue treatment rate and earlier recrudescence, resulting in a shorter period between the initial treatment and rescue treatment. Presumably this change occurs because of the shorter time available for the host to mount an immune response to the PfEMP1 variants expressed by the parasites. Although the treatment outcomes worsened when a lower fever threshold was used, the transmission potential of infections decreased as a result of the lower gametocyte densities and maturation time. The differences in transmission potential observed between parasite genotypes also tended to decrease with lower fever thresholds. Again, this is related to the increase in treatment failure and the earlier rescue treatment of infections.
The results of this study are based on output from a mathematical model and as such are dependent on the assumptions of the model. Two of these assumptions may potentially restrict the generalization of the results presented. First, the model mimics the dynamics of P. falciparum infections in naive hosts. As previously stated,15 this reflects situations in low malaria transmission areas, but may potentially over-estimate the failure rates of SP for different parasite genotypes in high transmission settings where a significant proportion of population are semi-immune and tend to respond better to treatment than naive individuals. As far as transmission is concerned, uncertainty about how the rate of gametocytogenesis changes as immunity develops, and the effect that immunity may have on infectivity of gametocytes to mosquitoes, restricts our ability to predict how the model output may change for simulations mimicking a partially immune host. However, one of the features of partial immunity is the ability to carry parasites without symptoms. This ability could prolong the period before individuals become symptomatic, thereby increasing the number of mature gametocytes produced prior to treatment, and/or it may allow a larger proportion of patients with resistant parasites to remain asymptomatic following treatment, further increasing the transmission potential of these parasites. Further work is required to better quantify the transmission potential of SP-resistant parasites in semi-immune hosts. The second assumption which needs consideration is that the model only simulates single clone infections. This is not an appropriate situation for high transmission areas when multiple clone infections are common.
For transmission settings and individuals fitting within the assumptions of the model, the results of this theoretical study indicate that there may be a significant proportion of patients who carry and transmit resistant parasites, but remain asymptomatic for extended periods. These patients fall into two categories: those who eventually develop clinical symptoms and those who remain asymptomatic. While the current policy for field trials is to provide rescue treatment to individuals who have detectable parasites or who become symptomatic during the follow-up,25 treatment of patients in a ‘non-research setting’ relies on patients returning to the health practitioner if symptoms re-develop. Very rarely are patients contacted for a follow-up parasitological sample. As a result, members of the community may carry and transmit resistant parasites even after treatment resolves their symptoms. The model suggests that infections caused by parasites carrying a triple mutation in DHFR and either 0 or 1 mutations in DHPS are most likely to persist for prolonged periods without symptomatic presentation. The results indicate that the infection dynamics following SP treatment are a key factor in determining the transmission potential of an infection. Prolonged asymptomatic recrudescence, as reported for moderately resistant parasites, provides this subset of parasites with an advantage over other parasites allowing them to expand relatively unnoticed.
The higher transmission potential associated with parasites having three mutations in DHFR compared with those with fewer DHFR mutations creates a favourable environment for the rapid spread of an introduced triple mutant through a less resistant parasite population. This advantage may have contributed to the selective sweep of the triple DHFR mutant which has occurred across continents.13
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None to declare.
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Acknowledgements |
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M. L. Gatton is funded by a University of Queensland Post-doctoral Research Fellowship. This work was supported in part by NIH grant AI047500-04. We would like to thank Dr Laura Martin for her critical review of the manuscript.
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References |
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