Plasmodium falciparum pfmdr1 amplification, mefloquine resistance, and parasite fitness.

Mefloquine is widely used in combination with artemisinin derivatives for the treatment of falciparum malaria. Mefloquine resistance in Plasmodium falciparum has been related to increased copy numbers of multidrug-resistant gene 1 (pfmdr1). We studied the ex vivo dynamics of pfmdr1 gene amplification in culture-adapted P. falciparum in relation to mefloquine resistance and parasite fitness. A Thai P. falciparum isolate (isolate TM036) was assessed by the use of multiple genetic markers as a single genotype. Resistance was selected by exposure to stepwise increasing concentrations of mefloquine up to 30 ng/ml in continuous culture. The pfmdr1 gene copy numbers increased as susceptibility to mefloquine declined (P = 0.03). No codon mutations at positions 86, 184, 1034, 1042, and 1246 in the pfmdr1 gene were detected. Two subclones of selected parasites (average copy numbers, 2.3 and 3.1, respectively) showed a fitness disadvantage when they were grown together with the original parasites containing a single pfmdr1 gene copy in the absence of mefloquine; the multiplication rates were 6.3% and 8.7% lower, respectively (P < 0.01). Modeling of the dynamics of the pfmdr1 copy numbers over time in relation to the relative fitness of the parasites suggested that net pfmdr1 gene amplification from one to two copies occurs once in every 10(8) parasites and that amplification from two to three copies occurs once in every 10(3) parasites. pfmdr1 gene amplification in P. falciparum is a frequent event and confers mefloquine resistance. Parasites with multiple copies of the pfmdr1 gene have decreased survival fitness in the absence of drug pressure.

The role of simple mathematical models in malaria elimination strategy design.

BACKGROUND: Malaria has recently been identified as a candidate for global eradication. This process will take the form of a series of national eliminations. Key issues must be considered specifically for elimination strategy when compared to the control of disease. Namely the spread of drug resistance, data scarcity and the adverse effects of failed elimination attempts. Mathematical models of various levels of complexity have been produced to consider the control and elimination of malaria infection. If available, detailed data on malaria transmission (such as the vector life cycle and behaviour, human population behaviour, the acquisition and decay of immunity, heterogeneities in transmission intensity, age profiles of clinical and subclinical infection) can be used to populate complex transmission models that can then be used to design control strategy. However, in many malaria countries reliable data are not available and policy must be formed based on information like an estimate of the average parasite prevalence. METHODS: A simple deterministic model, that requires data in the form of a single estimate of parasite prevalence as an input, is developed for the purpose of comparison with other more complex models. The model is designed to include key aspects of malaria transmission and integrated control. RESULTS: The simple model is shown to have similar short-term dynamic behaviour to three complex models. The model is used to demonstrate the potential of alternative methods of delivery of controls. The adverse effects on clinical infection and spread of resistance are predicted for failed elimination attempts. Since elimination strategies present an increased risk of the spread of drug resistance, the model is used to demonstrate the population level protective effect of multiple controls against this very serious threat. CONCLUSION: A simple model structure for the elimination of malaria is suitable for situations where data are sparse yet strategy design requirements are urgent with the caveat that more complex models, populated with new data, would provide more information, especially in the long-term.

Hyperparasitaemia and low dosing are an important source of anti-malarial drug resistance.

BACKGROUND: Preventing the emergence of anti-malarial drug resistance is critical for the success of current malaria elimination efforts. Prevention strategies have focused predominantly on qualitative factors, such as choice of drugs, use of combinations and deployment of multiple first-line treatments. The importance of anti-malarial treatment dosing has been underappreciated. Treatment recommendations are often for the lowest doses that produce "satisfactory" results. METHODS: The probability of de-novo resistant malaria parasites surviving and transmitting depends on the relationship between their degree of resistance and the blood concentration profiles of the anti-malarial drug to which they are exposed. The conditions required for the in-vivo selection of de-novo emergent resistant malaria parasites were examined and relative probabilities assessed. RESULTS: Recrudescence is essential for the transmission of de-novo resistance. For rapidly eliminated anti-malarials high-grade resistance can arise from a single drug exposure, but low-grade resistance can arise only from repeated inadequate treatments. Resistance to artemisinins is, therefore, unlikely to emerge with single drug exposures. Hyperparasitaemic patients are an important source of de-novo anti-malarial drug resistance. Their parasite populations are larger, their control of the infection insufficient, and their rates of recrudescence following anti-malarial treatment are high. As use of substandard drugs, poor adherence, unusual pharmacokinetics, and inadequate immune responses are host characteristics, likely to pertain to each recurrence of infection, a small subgroup of patients provides the particular circumstances conducive to de-novo resistance selection and transmission. CONCLUSION: Current dosing recommendations provide a resistance selection opportunity in those patients with low drug levels and high parasite burdens (often children or pregnant women). Patients with hyperparasitaemia who receive outpatient treatments provide the greatest risk of selecting de-novo resistant parasites. This emphasizes the importance of ensuring that only quality-assured anti-malarial combinations are used, that treatment doses are optimized on the basis of pharmacodynamic and pharmacokinetic assessments in the target populations, and that patients with heavy parasite burdens are identified and receive sufficient treatment to prevent recrudescence.

Gene amplification of the multidrug resistance 1 gene of Plasmodium vivax isolates from Thailand, Laos, and Myanmar.

Plasmodium vivax mdr1 gene amplification, quantified by real-time PCR, was significantly more common on the western Thailand border (6 of 66 samples), where mefloquine pressure has been intense, than elsewhere in southeast Asia (3 of 149; P = 0.02). Five coding mutations in pvmdr1, independent of gene amplification, were also found.

Spread of anti-malarial drug resistance: mathematical model with implications for ACT drug policies.

BACKGROUND: Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of malaria endemicity and in areas with the highest prevalence of drug resistance like borders of Thailand are certainly valuable. Formulating an appropriate dynamic data-driven model is a powerful predictive tool for exploring the impact of these strategies quantitatively. METHODS: A comprehensive model was constructed incorporating important epidemiological and biological factors of human, mosquito, parasite and treatment. The iterative process of developing the model, identifying data needed, and parameterization has been taken to strongly link the model to the empirical evidence. The model provides quantitative measures of outcomes, such as malaria prevalence/incidence and treatment failure, and illustrates the spread of resistance in low and high transmission settings. The model was used to evaluate different anti-malarial policy options focusing on ACT deployment. RESULTS: The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and treatment failure is the main force driving the spread of drug resistance. In low transmission settings, ACT slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. In the high transmission settings, however, drug resistance is driven by the proportion of the human population with a residual drug level, which gives resistant parasites some survival advantage. The spread of drug resistance could be slowed down by controlling presumptive drug use and avoiding the use of combination therapies containing drugs with mismatched half-lives, together with reducing malaria transmission through vector control measures. CONCLUSION: This paper has demonstrated the use of a comprehensive mathematical model to describe malaria transmission and the spread of drug resistance. The model is strongly linked to the empirical evidence obtained from extensive data available from various sources. This model can be a useful tool to inform the design of treatment policies, particularly at a time when ACT has been endorsed by WHO as first-line treatment for falciparum malaria worldwide.

Estimation of the total parasite biomass in acute falciparum malaria from plasma PfHRP2.

BACKGROUND: In falciparum malaria sequestration of erythrocytes containing mature forms of Plasmodium falciparum in the microvasculature of vital organs is central to pathology, but quantitation of this hidden sequestered parasite load in vivo has not previously been possible. The peripheral blood parasite count measures only the circulating, relatively non-pathogenic parasite numbers. P. falciparum releases a specific histidine-rich protein (PfHRP2) into plasma. Quantitative measurement of plasma PfHRP2 concentrations may reflect the total parasite biomass in falciparum malaria. METHODS AND FINDINGS: We measured plasma concentrations of PfHRP2, using a quantitative antigen-capture enzyme-linked immunosorbent assay, in 337 adult patients with falciparum malaria of varying severity hospitalised on the Thai-Burmese border. Based on in vitro production rates, we constructed a model to link this measure to the total parasite burden in the patient. The estimated geometric mean parasite burden was 7 x 10(11) (95% confidence interval [CI] 5.8 x 10(11) to 8.5 x 10(11)) parasites per body, and was over six times higher in severe malaria (geometric mean 1.7 x 10(12), 95% CI 1.3 x 10(12) to 2.3 x 10(12)) than in patients hospitalised without signs of severity (geometric mean 2.8 x 10(11), 95% CI 2.3 x 10(11) to 3.5 x 10(11); p < 0.001). Parasite burden was highest in patients who died (geometric mean 3.4 x 10(12), 95% CI 1.9 x 10(12) to 6.3 x 10(12); p = 0.03). The calculated number of sequestered parasites increased with disease severity and was higher in patients with late developmental stages of P. falciparum present on peripheral blood smears. Comparing model and laboratory estimates of the time of sequestration suggested that admission to hospital with uncomplicated malaria often follows schizogony-but in severe malaria is unrelated to stage of parasite development. CONCLUSION: Plasma PfHRP2 concentrations may be used to estimate the total body parasite biomass in acute falciparum malaria. Severe malaria results from extensive sequestration of parasitised erythrocytes.

Stage-dependent production and release of histidine-rich protein 2 by Plasmodium falciparum.

Because of their sequestration in the microcirculation, the pathogenic late stages of Plasmodium falciparum are under-represented in peripheral blood samples from patients with falciparum malaria. Excreted products of the parasite might help to estimate this sequestered biomass. We quantified the stage-dependent production and release per parasite of P. falciparum histidine-rich protein 2 (PfHRP2) with the objective of measuring the sequestered biomass. A simple method to relate parasite stage to parasite age was developed to facilitate this. In four isolates of P. falciparum, the median (range) PfHRP2 content was 2.0fg (0.5-4.3fg) for a young ring stage infected erythrocyte, and 5.4fg (2.1-10.2fg) for the schizont stage. The amount of PfHRP2 in the parasitized erythrocyte increased most during development to the mature trophozoite stage. The median (range) amount of PfHRP2 secreted per parasite per entire erythrocytic cycle was 5.2fg (1.1-13.0fg). A median of 89% of the total PfHRP2 was excreted at the moment of schizont rupture. This assessment of the stage-dependent release of PfHRP2 is an essential prerequisite for future studies aimed at estimating the total patient parasite mass from the peripheral blood PfHRP2 concentration.

Antimalarial drug resistance, artemisinin-based combination therapy, and the contribution of modeling to elucidating policy choices.

Increasing resistance of Plasmodium falciparum malaria to antimalarial drugs is posing a major threat to the global effort to "Roll Back Malaria". Chloroquine and sulfadoxine-pyrimethamine (SP) are being rendered increasingly ineffective, resulting in increasing morbidity, mortality, and economic and social costs. One strategy advocated for delaying the development of resistance to the remaining armory of effective drugs is the wide-scale deployment of artemisinin-based combination therapy. However, the cost of these combinations are higher than most of the currently used monotherapies and alternative non-artemisinin-based combinations. In addition, uncertainty about the actual impact in real-life settings has made them a controversial choice for first-line treatment. The difficulties in measuring the burden of drug resistance and predicting the impact of strategies aimed at its reduction are outlined, and a mathematical model is introduced that is being designed to address these issues and to clarify policy options.

Strategies for diagnosis and treatment of suspected leptospirosis: a cost-benefit analysis.

BACKGROUND: Symptoms and signs of leptospirosis are non-specific. Several diagnostic tests for leptospirosis are available and in some instances are being used prior to treatment of leptospirosis-suspected patients. There is therefore a need to evaluate the cost-effectiveness of the different treatment strategies in order to avoid misuse of scarce resources and ensure best possible health outcomes for patients. METHODS: The study population was adult patients, presented with uncomplicated acute febrile illness, without an obvious focus of infection or malaria or typical dengue infection. We compared the cost and effectiveness of 5 management strategies: 1) no patients tested or given antibiotic treatment; 2) all patients given empirical doxycycline treatment; patients given doxycycline when a patient is tested positive for leptospirosis using: 3) lateral flow; 4) MCAT; 5) latex test. The framework used is a cost-benefit analysis, accounting for all direct medical costs in diagnosing and treating patients suspected of leptospirosis. Outcomes are measured in length of fever after treatment which is then converted to productivity losses to capture the full economic costs. FINDINGS: Empirical doxycycline treatment was the most efficient strategy, being both the least costly alternative and the one that resulted in the shortest duration of fever. The limited sensitivity of all three diagnostic tests implied that their use to guide treatment was not cost-effective. The most influential parameter driving these results was the cost of treating patients with complications for patients who did not receive adequate treatment as a result of incorrect diagnosis or a strategy of no-antibiotic-treatment. CONCLUSIONS: Clinicians should continue treating suspected cases of leptospirosis on an empirical basis. This conclusion holds true as long as policy makers are not prioritizing the reduction of use of antibiotics, in which case the use of the latex test would be the most efficient strategy.

The role of mathematical modelling in guiding the science and economics of malaria elimination.

Unprecedented efforts are now underway to eliminate malaria from many regions. Despite the enormous financial resources committed, if malaria elimination is perceived as failing it is likely that this funding will not be sustained. It is imperative that methods are developed to use the limited data available to design site-specific, cost-effective elimination programmes. Mathematical modelling is a way of including mechanistic understanding to use available data to make predictions. Different strategies can be evaluated much more rapidly than is possible through trial and error in the field. Mathematical modelling has great potential as a tool to guide and inform current elimination efforts. Economic modelling weighs costs against characterised effects or predicted benefits in order to determine the most cost-efficient strategy but has traditionally used static models of disease not suitable for elimination. Dynamic mathematical modelling and economic modelling techniques need to be combined to contribute most effectively to ongoing policy discussions. We review the role of modelling in previous malaria control efforts as well as the unique nature of elimination and the consequent need for its explicit modelling, and emphasise the importance of good disease surveillance. The difficulties and complexities of economic evaluation of malaria control, particularly the end stages of elimination, are discussed.

Probability of emergence of antimalarial resistance in different stages of the parasite life cycle.

Understanding the evolution of drug resistance in malaria is a central area of study at the intersection of evolution and medicine. Antimalarial drug resistance is a major threat to malaria control and directly related to trends in malaria attributable mortality. Artemisinin combination therapies (ACT) are now recommended worldwide as first line treatment for uncomplicated malaria, and losing them to resistance would be a disaster for malaria control. Understanding the emergence and spread of antimalarial drug resistance in the context of different scenarios of antimalarial drug use is essential for the development of strategies protecting ACTs. In this study, we review the basic mechanisms of resistance emergence and describe several simple equations that can be used to estimate the probabilities of de novo resistance mutations at three stages of the parasite life cycle: sporozoite, hepatic merozoite and asexual blood stages; we discuss the factors that affect parasite survival in a single host in the context of different levels of antimalarial drug use, immunity and parasitaemia. We show that in the absence of drug effects, and despite very different parasite numbers, the probability of resistance emerging at each stage is very low and similar in all stages (for example per-infection probability of 10(-10)-10(-9) if the per-parasite chance of mutation is 10(-10) per asexual division). However, under the selective pressure provided by antimalarial treatment and particularly in the presence of hyperparasitaemia, the probability of resistance emerging in the blood stage of the parasite can be approximately five orders of magnitude higher than in the absence of drugs. Detailed models built upon these basic methods should allow us to assess the relative probabilities of resistance emergence in the different phases of the parasite life cycle.

Pharmacokinetic and pharmacodynamic assessment of co-amoxiclav in the treatment of melioidosis.

OBJECTIVES: We conducted a prospective pharmacokinetic study of oral co-amoxiclav in patients with melioidosis to determine the optimal dosage and dosing interval in this potentially fatal infection. PATIENTS AND METHODS: Serial plasma concentrations were measured after administration of two 1 g tablets of Augmentin (1750 mg of amoxicillin and 250 mg of clavulanate) to 14 adult patients with melioidosis. Monte Carlo simulation was used to predict the concentration of each drug following multiple doses of co-amoxiclav at different dosages and dose intervals. The proportion of the dose-interval above MIC (T > MIC) was calculated from 10,000 simulated subject plasma concentration profiles together with chequerboard MIC data from 46 clinical isolates and four reference strains of Burkholderia pseudomallei. RESULTS: The median (range) observed maximum plasma concentrations of amoxicillin and clavulanate were 11.5 (3.3-40.2) mg/L and 5.1 (0.8-12.1) mg/L, respectively. The median (range) elimination half-lives were 94 (73-215) and 89 (57-140) min, respectively. Simulation indicated that co-amoxiclav 1750/250 mg given at 4, 6, 8 or 12 hourly dosing intervals would be associated with a T > MIC of < or = 50% in 0.7%, 2.8%, 8.6% and 33.2% of patients, respectively. Corresponding proportions for T > MIC of > or = 90% were 95.8%, 78.6%, 50.2% and 10.8%, respectively. CONCLUSIONS: The dosing interval for co-amoxiclav (750/250 mg) in melioidosis should not be greater than 6 h.