Melbourne School of Population and Global Health - Theses
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ItemAnopheles salivary antibodies as biomarkers of vector exposure and malaria transmission in the Greater Mekong Subregion( 2023-06)Measurement of human antibodies against Anopheles salivary antigens has been suggested as a sensitive and feasible alternative to measure human biting rates (HBR), improving on logistically challenging gold-standard entomological approaches that provide crude measures of total exposure to vector bites. To understand how this approach could be used to advance the Greater Mekong Subregion’s (GMS) malaria elimination agenda, in this thesis I sought to formally quantify the association between anti-Anopheles salivary antibodies and HBR, and investigate the programmatic application of these antibodies for serosurveillance of malaria transmission and as outcomes in vector control intervention effectiveness trials. Firstly, I performed a systematic review with multilevel modelling and quantified a positive non-linear association between antibodies against gSG6 (from the dominant African vector An. gambiae) and HBR, as well as metrics of malaria transmission. However, I identified that this association was weaker outside Africa where An. gambiae is absent. These findings provide evidence that anti-Anopheles salivary antibodies could serve as biomarkers of Anopheles biting exposure, but novel species-specific antigens may be needed to estimate HBR in the GMS. Secondly, I measured the seroprevalence of antibodies against Anopheles salivary antigen gSG6 in 104 villages in Southeast Myanmar and employed Bayesian geostatistical modelling to quantify the micro-heterogeneity in Anopheles biting exposure, which was found to be high (mean: 66%) yet heterogeneous (range: 9-99%). I combined vector (gSG6) and transmission-stage malaria parasite (CSP) antibody biomarkers with PCR-detectable infections in a novel joint modelling framework to predict malaria transmission across Southeast Myanmar. These maps identify several foci of ongoing transmission and could be used to micro-stratify malaria risk for targeting interventions. Thirdly, I quantified the effect of topical insect repellent on the levels of antibodies against Anopheles salivary antigen gSG6. By estimating a series of lagged effects of repellent distribution (to model gradual antibody decay from prolonged use), I observed reduced antibody levels after 6-months of repellent use, particularly for high-risk participants (migrants and forest-goers). These findings suggest anti-Anopheles salivary antibodies could be an informative trial outcome measure and provide important parameters on antibody decay dynamics to inform the design of future vector control interventions effectiveness trials. Finally, I directly quantified the boosting and decay of IgG and IgM antibodies against a series of Anopheles species-specific salivary antigens in response to controlled biting exposure from dominant vectors of the GMS. I found that antibody levels decayed overall, but were boosted with and following mosquito biting. These associations were similar across Anopheles species-specific salivary antigens and antibody isotypes, and species-specific antibodies levels were all highly correlated (spearman’s rho>0.8). These findings provide evidence that anti-Anopheles species-specific salivary antibodies could be sensitive biomarkers of exposure to Anopheles bites in the GMS. Collectively, the findings of this thesis provide a comprehensive investigation of anti-Anopheles salivary antibodies as biomarkers of exposure to Anopheles bites in the GMS. These findings directly quantify (previously-assumed) associations with entomological and malariometric measures of exposure and transmission, and support the application of these antibodies as endpoints in surveillance programs and vector control intervention effectiveness trials.
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ItemDetermining effective and safe treatments for vivax malaria using a modelling and simulation framework( 2023-02)Background Malaria is a mosquito-borne disease found in 84 countries. Almost half of the malaria cases in Asia and South America are due to Plasmodium vivax (P. vivax). P. vivax parasites stay dormant in the liver, releasing parasites into the bloodstream weeks to months after the initial infection. Primaquine is the only widely available drug that targets the parasites in the liver, however, the recommended 14-day course of primaquine has low adherence and there is an increased risk of haemolysis in individuals with glucose-6-phosphate dehydrogenase (G6PD) deficiency. This thesis aims to improve the effectiveness and safety of primaquine regimens for P. vivax by better understanding the effect of adherence on effectiveness through an individual patient data meta-analysis and by using a complex mechanistic within-host model to characterise red blood cell (RBC) dynamics and explore the safety of primaquine regimens for G6PD-deficient individuals. Methods Individual patient data from vivax efficacy studies were pooled to investigate the impact of adherence to primaquine on the risk of P. vivax recurrence between days 7 and 90 after starting treatment. Adherence to primaquine was assessed based on supervision status and the total versus expected mg/kg dose administered. The safety of primaquine dosing schemes was explored through the development of a within-host model that captures the effect of primaquine on RBC production and destruction. The mechanistic RBC model was fitted to haemoglobin and reticulocyte measurements from a regimen-adaptive trial of ascending primaquine doses in G6PD-deficient individuals using a Bayesian hierarchical framework. The posterior distributions of the model parameters were then used to determine safe primaquine dosing strategies for G6PD-deficient individuals. Mechanistic within-host models are valuable tools to determine the safety and efficacy of a drug, guiding the development of optimal treatment regimens. However, there are challenges with parameter estimation of these complex non-linear models. Two Bayesian hierarchical approaches for model fitting and parameter estimation of complex non-linear mechanistic models were assessed using a simulation study of hypothetical patients receiving primaquine. The one-stage method analyses all individual data profiles simultaneously to estimate both the population and individual-level parameters, whereas the two-stage approach fits the within-host model to each individual data profile separately and uses the individual-level parameter estimates as proposal distributions to derive the population-level parameters. Results Individual data from 32 studies involving 6,917 patients treated with primaquine showed that reduced adherence increased the risk of vivax recurrence. The Adjusted Hazard Ratio (AHR) was a 2.3-fold [95% Confidence Interval (CI): 1.8-2.9] to 3.2-fold [95% CI: 1.5-6.7] increase in the rate of recurrence when adherence was poor (<50%), defined by supervision or total mg/kg dose, respectively. Mechanistic modelling of the RBC profiles of 24 subjects from the Primaquine Challenge Study showed that ascending regimens of primaquine resulted in predictable self-limiting haemolysis and should be safer than a single high dose. In particular, the mechanistic model describing the RBC dynamics estimated that high primaquine doses (45 mg base) resulted in a reduction of RBC lifespan of approximately 35%. Simulations from the model determined an optimal dosing regimen of 5 mg/kg over 14 days was safe for individuals with dominant G6PD deficiency variants in Southeast Asia. In a simulation study comparing two estimation methods for fitting mechanistic within-host models to longitudinal data in a Bayesian hierarchical framework, the two-stage approach produced comparable point estimates to the one-stage approach for estimation of parameters of the within-host RBC model and was faster to implement. Conclusions This thesis provides evidence to improve the effectiveness and safety of primaquine radical cure for P. vivax; through the importance of enhanced adherence and the potential reduction of haemolysis in vulnerable G6PD-deficient individuals with a proposed ascending dose regimen. For estimation of computationally prohibitive mechanistic within-host models, the two-stage estimation method may be a suitable alternative.
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ItemQuantification of the dynamics of antibody response to malaria in pregnant women( 2020)Malaria remains a major public health threat with pregnant women and young children the most vulnerable populations. Approximately 125 million pregnant women are at risk of malaria infection every year. Immunity to malaria can significantly reduce the severity of malaria symptoms. This has long motivated efforts to better understand the immune response to malaria in order to develop an efficacious antimalarial vaccine. However, the complex behaviour of the immune system, particularly in pregnant women, has hindered identifying the underpinning mechanisms of the immune response to malaria infection. This project investigates antibody-mediated immunity in pregnant women who attended antenatal clinics located at the Thai-Myanmar border, where malaria transmission is low and P. falciparum and P. vivax (the most prevalent malaria species globally) coexist. Antibody responses during pregnancy to six parasite antigens were measured for 250 pregnant women in a median of 7 samples per woman (range 2 to 13) over the gestation period. A multivariate mixture linear mixed model was fitted to longitudinal antibody data of 250 pregnant women to characterise the highly dynamic antibody responses using a Bayesian approach. The posterior distribution of the parameters estimated via Markov chain Monte Carlo (MCMC) simulations were used to classify the antibody responses. The results show that the infectious status of a woman during follow-up is a key factor influencing the classification of the joint behaviour of the antibody responses. Hence, the two malaria exposure categories (exposed to infection during pregnancy defined as a case and those non-exposed defined as controls) were represented by the two antibody profile clusters. Using a manually developed code, entropy values were computed for each antibody, with which, the contribution made by each antibody on the classification was assessed. The antibodies PfMSP3 and PvAMA1 which maintained less dynamic antibody profiles significantly influenced the classification. However, these antibodies identified controls exceptionally well but did not perform well for the cases. For sero-surveillance, antibodies which best identify the cases are required, hence, the study was extended in performing classification based on all possible univariate and multivariate combinations of the six antibodies. The bivariate combination PfAMA1 and PfVAR2CSA resulted in identifying the majority of cases by contributing towards identification of potential biomarker(s) for sero-surveillance of recent exposure to malaria during pregnancy. Therefore, the bivariate combination of antibodies, PfAMA1 and PfVAR2CSA should be used in the field, particularly for accurate malaria surveillance of pregnant women living in low malaria transmission settings. This could lead to the early detection and treatment of malaria infections in pregnant women, reducing transmission and thereby progressing towards elimination of malaria.