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BACKGROUND: Altered sensitivity to multiple antimalarial drugs is mediated by polymorphisms in pfmdr1, which encodes the Plasmodium falciparum multidrug resistance transporter. In Africa the N86Y and D1246Y polymorphisms have been shown to be selected by treatment, with artemether-lumefantrine (AL) and dihydroartemisinin-piperaquine (DP) selecting for wild-type and mutant alleles, respectively. However, there has been little study of pfmdr1 haplotypes, in part because haplotype analyses are complicated by multiclonal infections. METHODS: We fit a haplotype frequency estimation model, which accounts for multiclonal infections, to the polymorphic pfmdr1 N86Y, Y184F, and D1246Y alleles in samples from a longitudinal trial comparing AL and DP to treat uncomplicated P falciparum malaria in Tororo, Uganda from 2007 to 2012. We regressed estimates onto covariates of trial arm and selective drug pressure. RESULTS: Yearly trends showed increasing frequency estimates for haplotypes with wild type pfmdr1 N86 and D1246 alleles and decreasing frequency estimates for haplotypes with the mutant pfmdr1 86Y allele. Considering days since prior therapy, we saw evidence suggestive of selection by AL for haplotypes with N86 combined with 184F, D1246, or both, and against all haplotypes with 86Y, and evidence suggestive of selection by DP for 86Y only when combined with Y184 and 1246Y (haplotype YYY) and against haplotypes NFD and NYY. CONCLUSIONS: Based on our model, AL selected several haplotypes containing N86, whereas DP selection was haplotype specific, demonstrating the importance of haplotype analyses. Inverse selective pressure of AL and DP on the complementary haplotypes NFD and YYY suggests that rotating artemisinin-based antimalarial combination regimens may be the best treatment option to prevent resistance selection.

We have integrated a bioengineered three-dimensional platform by generating multicellular cancer spheroids in a controlled microenvironment with a mathematical model to investigate confined tumour growth and to model its impact on cellular processes.

A critical step in the dissemination of ovarian cancer cells is the formation of multicellular spheroids from cells shed from the primary tumor. The objectives of this study were to establish and validate bioengineered three-dimensional (3D) microenvironments for culturing ovarian cancer cells in vitro and simultaneously to develop computational models describing the growth of multicellular spheroids in these bioengineered matrices. Cancer cells derived from human epithelial ovarian carcinoma were embedded within biomimetic hydrogels of varying stiffness and cultured for up to 4 weeks. Immunohistochemistry was used to quantify the dependence of cell proliferation and apoptosis on matrix stiffness, long-term culture and treatment with the anti-cancer drug paclitaxel. Two computational models were developed. In the first model, each spheroid was treated as an incompressible porous medium, whereas in the second model the concept of morphoelasticity was used to incorporate details about internal stresses and strains. Each model was formulated as a free boundary problem. Functional forms for cell proliferation and apoptosis motivated by the experimental work were applied and the predictions of both models compared with the output from the experiments. Both models simulated how the growth of cancer spheroids was influenced by mechanical and biochemical stimuli including matrix stiffness, culture time and treatment with paclitaxel. Our mathematical models provide new perspectives on previous experimental results and have informed the design of new 3D studies of multicellular cancer spheroids.

WWARN Artemisinin based Combination Therapy (ACT) Africa Baseline Study Group; International audience; Background: Artemisinin-resistant Plasmodium falciparum has emerged in the Greater Mekong sub-region and poses a major global public health threat. Slow parasite clearance is a key clinical manifestation of reduced susceptibility to artemisinin. This study was designed to establish the baseline values for clearance in patients from Sub-Saharan African countries with uncomplicated malaria treated with artemisinin-based combination therapies (ACTs). Methods: A literature review in PubMed was conducted in March 2013 to identify all prospective clinical trials (uncontrolled trials, controlled trials and randomized controlled trials), including ACTs conducted in Sub-Saharan Africa, between 1960 and 2012. Individual patient data from these studies were shared with the WorldWide Antimalarial Resistance Network (WWARN) and pooled using an a priori statistical analytical plan. Factors affecting early parasitological response were investigated using logistic regression with study sites fitted as a random effect. The risk of bias in included studies was evaluated based on study design, methodology and missing data. Results: In total, 29,493 patients from 84 clinical trials were included in the analysis, treated with artemether-lumefantrine (n = 13,664), artesunate-amodiaquine (n = 11,337) and dihydroartemisinin-piperaquine (n = 4,492). The overall parasite clearance rate was rapid. The parasite positivity rate (PPR) decreased from 59.7 % (95 % CI: 54.5–64.9) on day 1 to 6.7 % (95 % CI: 4.8–8.7) on day 2 and 0.9 % (95 % CI: 0.5–1.2) on day 3. The 95th percentile of observed day 3 PPR was 5.3 %. Independent risk factors predictive of day 3 positivity were: high baseline parasitaemia (adjusted odds ratio (AOR) = 1.16 (95 % CI: 1.08–1.25); per 2-fold increase in parasite density, P 37.5 °C) (AOR = 1.50 (95 % CI: 1.06–2.13), P = 0.022); severe anaemia (AOR = 2.04 (95 % CI: 1.21–3.44), P = 0.008); areas of low/moderate transmission setting (AOR = 2.71 (95 % CI: 1.38–5.36), P = 0.004); and treatment with the loose formulation of artesunate-amodiaquine (AOR = 2.27 (95 % CI: 1.14–4.51), P = 0.020, compared to dihydroartemisinin-piperaquine). Conclusions: The three ACTs assessed in this analysis continue to achieve rapid early parasitological clearance across the sites assessed in Sub-Saharan Africa. A threshold of 5 % day 3 parasite positivity from a minimum sample size of 50 patients provides a more sensitive benchmark in Sub-Saharan Africa compared to the current recommended threshold of 10 % to trigger further investigation of artemisinin susceptibility.

Malaria kills hundreds of thousands of people each year, yet is entirely curable given prompt treatment. Malaria parasites evolve resistance to antimalarial drugs, hence routine surveillance of antimalarial resistance is vital. The surveillance of parasite genetic markers of resistance provides an economical adjunct to clinical efficacy trials, and has the potential to resolve drug specific resistance ahead of clinical failure. To monitor spatiotemporal changes using genetic markers, frequencies of alleles and/or haplotypes and genotypes spanning multiple single nucleotide polymorphisms (SNPs) are required. However, multiclonal infections complicate frequency estimation, especially in highly endemic regions. With the aim of harnessing the full potential of genetic markers for the surveillance of antimalarial resistance, a statistical model to estimate frequencies is proposed. The model builds upon existing methods (reviewed in chapter 2), without reliance upon experimentally-derived estimates of the sample-wise multiplicities of infection (MOIs). Its ability to generate precise and accurate estimates within a Bayesian framework is documented in chapter 3. In chapter 4, the model is applied to data collected from a cohort of children enrolled in a longitudinal trial in Uganda, generating valuable insight into haplotype frequency trends. In chapter 5, the model is extended to investigate inter-child variability in the aforesaid cohort, revealing a small amount of inter-child variation. In chapter 6, the model is modified to enable the analysis of short-read sequencing data, with application to data from malaria patients in Northern Ghana, providing insight into the extent of within-host diversity and anti-folate resistance in the region. In summary, this thesis documents the development, application, extension and modification of a model designed to estimate population-level frequencies of P. falciparum alleles and multi-SNP haplotypes and genotypes within a Bayesian framework. It is hoped that the model and its proposed framework will provide a practical tool for surveillance of antimalarial resistance, as well as a foundation on which to develop further methods.