Anja Terlouw

Whole-genome scans provide evidence of adaptive evolution in Malawian Plasmodium falciparum isolates

Background Selection by host immunity and antimalarial drugs has driven extensive adaptive evolution in Plasmodium falciparum and continues to produce ever-changing landscapes of genetic variation. Methods We performed whole-genome sequencing of 69 P. falciparum isolates from Malawi and used population genetics approaches to investigate genetic diversity and population structure and identify loci under selection. Results High genetic diversity (π = 2.4 × 10−4), moderately high multiplicity of infection (2.7), and low linkage disequilibrium (500-bp) were observed in Chikhwawa District, Malawi, an area of high malaria transmission. Allele frequency–based tests provided evidence of recent population growth in Malawi and detected potential targets of host immunity and candidate vaccine antigens. Comparison of the sequence variation between isolates from Malawi and those from 5 geographically dispersed countries (Kenya, Burkina Faso, Mali, Cambodia, and Thailand) detected population genetic differences between Africa and Asia, within Southeast Asia, and within Africa. Haplotype-based tests of selection to sequence data from all 6 populations identified signals of directional selection at known drug-resistance loci, including pfcrt, pfdhps, pfmdr1, and pfgch1. Conclusions The sequence variations observed at drug-resistance loci reflect differences in each countrys historical use of antimalarial drugs and may be useful in formulating local malaria treatment guidelines.

Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi

Introduction In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design. Methods We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area. Findings We conducted five rounds of sampling, and tested 876 children aged 6–59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots—where predicted malaria prevalence was above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets. Conclusions Continuous malaria prevalence surveys using adaptive sampling increased malaria prevalence prediction accuracy. Results from the surveys were readily available after data collection. The tool can assist local managers to target malaria control interventions in areas with the greatest health impact and is ready for assessment in other diseases.

An in silico drug treatment model to assess the robustness of regional age-based dosing regimens for artemisinin-based combination therapies

The standard drug development process for antimalarials and other drugs uses weight-based dosing (mg/kg) to predict blood concentrations of the drug, and hence their effect. Consequently, the current World Health Organization Guidelines for the treatment of malaria [1] provide target doses and therapeutic dose ranges in mg/ kg/day. However, in resource-poor settings, age-based dosing is often employed instead of weight-based dosing because of the scarcity of correctly functioning weighing scales outside of clinical settings. Due to the wide variation in weight by age this approach inevitably results in over- and under-dosing of a proportion of the population. We have recently developed a modelling method to create statistically robust global and regional malaria-specific weight-for-age references representative of the malaria-endemic countries [2] and employed it to predict optimized age-based regimens for artemisinin-based combination therapies (ACTs) for case management of uncomplicated malaria (unpublished). The presented work now assesses the robustness of these age-based regimens using an in silico model of antimalarial drug treatment to predict treatment outcome based on individual infection parameters such as parasite numbers, variation in patient pharmacokinetics, and parasite variation in their drug sensitivity [3]. This extended pharmacokinetic/pharmakodynamic model for ACTs allowed us to investigate extreme treatment scenarios in a large number of patients over long follow-up periods that for ethical reasons could not be applied in clinical trials: typical examples include poor adherence (e.g. delayed, reduced or missed doses) or administration of doses above or below recommended therapeutic dose ranges and particularly in most vulnerable individuals such as infants and young children. Pharmacological modelling of antimalarial treatment cannot replace the gold standard of clinical trials, but the model outputs can identify patient groups that are at higher risk of treatment failure due to under-dosing or adverse events due to over-dosing. We acknowledge the Medical Research Council for funding of this work.

THE IMPACT PROJECT: IMPROVING THE IMPACT OF EXISTING MALARIA PRODUCTS – ACTS

Background The antimalarial dihydroartemisinin-piperaquine (DHA-PPQ) is one of the recommended drugs to treat uncomplicated Plasmodium falciparum malaria. However, DHA-PPQ has a relatively narrow, poorly defined therapeutic dose range and it is unclear whether PPQ concentration-dependent cardio-toxicity (QTc prolongation) poses a clinical risk for specific subgroups. Uncertainty about the exact safe upper PPQ concentration threshold and recognition of the vulnerability of children has led WHO to consider a complex weight-based dosing regimen. These complex dosing schemes may challenge DHA-PPQ introduction into national control programmes. It also highlights the urgent need to standardise the dose optimisation process. Methods The IMPACT project aims to determine the frequency and severity of DHA-PPQ cardio-toxicity, and its correlation with dose and drug concentration through WWARN-pooled patient-level pharmacokinetic-pharmaco-dynamic safety analysis, and antiretroviral drug interactions using all available data. Using the established WWARN platform, an open study group has been established to allow data sharing and joint analyses by data contributors and other key stakeholders. Results We will present a progress update of the IMPACT project and associated WWARN DHA-PPQ safety group. Findings will inform an up-to-date safety profile and upper PPQ dose thresholds across key risk groups and identify remaining research priorities. DHA-PPQ dosing challenges, lessons learnt, and opportunities to address these through a more standardised process for antimalarial dose optimisation will be reviewed, and awareness of dose optimisation research priorities will be raised among researchers, funders and control programmes. Conclusions This work will help inform policy decisions on DHA-PPQ dosing regimens and help demonstrate the importance of identifying global research priorities for targeted antimalarial safety studies and of integrating pooled individual level safety analyses into WWARNs global efficacy data platform, as a powerful standardised process for dose optimisation.