Seasonal malaria chemoprevention and the spread of Plasmodium falciparum quintuple-mutant parasites resistant to sulfadoxine-pyrimethamine: a modelling study.

BACKGROUND: Seasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine plus amodiaquine prevents millions of clinical malaria cases in children younger than 5 years in Africa's Sahel region. However, Plasmodium falciparum parasites partially resistant to sulfadoxine-pyrimethamine (with quintuple mutations) potentially threaten the protective effectiveness of SMC. We evaluated the spread of quintuple-mutant parasites and the clinical consequences. METHODS: We used an individual-based malaria transmission model with explicit parasite dynamics and drug pharmacological models to identify and quantify the influence of factors driving quintuple-mutant spread and predict the time needed for the mutant to spread from 1% to 50% of inoculations for several SMC deployment strategies. We estimated the impact of this spread on SMC effectiveness against clinical malaria. FINDINGS: Higher transmission intensity, SMC coverage, and expanded age range of chemoprevention promoted mutant spread. When SMC was implemented in a high-transmission setting (40% parasite prevalence in children aged 2-10 years) with four monthly cycles to children aged 3 months to 5 years (with 95% initial coverage declining each cycle), the quintuple mutant required 53·1 years (95% CI 50·5-56·0) to spread from 1% to 50% of inoculations. This time increased in lower-transmission settings and reduced by half when SMC was extended to children aged 3 months to 10 years, or reduced by 10-13 years when an additional monthly cycle of SMC was deployed. For the same setting, the effective reduction in clinical cases in children receiving SMC was 79·0% (95% CI 77·8-80·8) and 60·4% (58·6-62·3) during the months of SMC implementation when the quintuple mutant was absent or fixed in the population, respectively. INTERPRETATION: SMC with sulfadoxine-pyrimethamine plus amodiaquine leads to a relatively slow spread of sulfadoxine-pyrimethamine-resistant quintuple mutants and remains effective at preventing clinical malaria despite the mutant spread. SMC with sulfadoxine-pyrimethamine plus amodiaquine should be considered in seasonal settings where this mutant is already prevalent. FUNDING: Swiss National Science Foundation and Marie Curie Individual Fellowship.

Design and selection of drug properties to increase the public health impact of next-generation seasonal malaria chemoprevention: a modelling study.

BACKGROUND: Seasonal malaria chemoprevention (SMC) is recommended for disease control in settings with moderate to high Plasmodium falciparum transmission and currently depends on the administration of sulfadoxine-pyrimethamine plus amodiaquine. However, poor regimen adherence and the increased frequency of parasite mutations conferring sulfadoxine-pyrimethamine resistance might threaten the effectiveness of SMC. Guidance is needed to de-risk the development of drug compounds for malaria prevention. We aimed to provide guidance for the early prioritisation of new and alternative SMC drugs and their target product profiles. METHODS: In this modelling study, we combined an individual-based malaria transmission model that has explicit parasite growth with drug pharmacokinetic and pharmacodynamic models. We modelled SMC drug attributes for several possible modes of action, linked to their potential public health impact. Global sensitivity analyses identified trade-offs between drug elimination half-life, maximum parasite killing effect, and SMC coverage, and optimisation identified minimum requirements to maximise malaria burden reductions. FINDINGS: Model predictions show that preventing infection for the entire period between SMC cycles is more important than drug curative efficacy for clinical disease effectiveness outcomes, but similarly important for impact on prevalence. When children younger than 5 years receive four SMC cycles with high levels of coverage (ie, 69% of children receiving all cycles), drug candidates require a duration of protection half-life higher than 23 days (elimination half-life >10 days) to achieve reductions higher than 75% in clinical incidence and severe disease (measured over the intervention period in the target population, compared with no intervention across a range of modelled scenarios). High coverage is crucial to achieve these targets, requiring more than 60% of children to receive all SMC cycles and more than 90% of children to receive at least one cycle regardless of the protection duration of the drug. INTERPRETATION: Although efficacy is crucial for malaria prevalence reductions, chemoprevention development should select drug candidates for their duration of protection to maximise burden reductions, with the duration half-life determining cycle timing. Explicitly designing or selecting drug properties to increase community uptake is paramount. FUNDING: Bill & Melinda Gates Foundation and the Swiss National Science Foundation.

Patient variability in the blood-stage dynamics of Plasmodium falciparum captured by clustering historical data.

BACKGROUND: Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation. METHODS: Patients' parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization). RESULTS: To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles. CONCLUSION: This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models.

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum.

The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure-treatment properties, biological factors, transmission intensity, and access to treatment-obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.