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Title: Computer modelling approaches for improving analysis of 1 anti-malarial clinical trials
Author: Jones, Sam
ISNI:       0000 0004 8501 7556
Awarding Body: Liverpool School of Tropical Medicine
Current Institution: Liverpool School of Tropical Medicine
Date of Award: 2019
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Clinical trials of treatments for Plasmodium falciparum are an integral aspect of a continually evolving evidence base that informs public health policy with the aim of reducing malaria morbidity, mortality, preventing the emergence of parasite resistance to drugs and, eventually, permitting elimination of the disease. Despite their importance, obtaining useful information from in vivo trials can be hindered through methodological gaps that make it difficult to obtain or analyse results (through an inability to quantify important parameters in vivo), cost, required patient numbers or ethical considerations. This thesis uses a computer modelling approach to address two key research problems relating to in vivo trials: Firstly, it quantifies the accuracy failure rate estimates obtained during trials for routine monitoring of artemisinin-based combination therapy (ACT) efficacy in cases of uncomplicated malaria, noting that currently available methods for genotyping patient blood samples are imperfect, and that patients can be infected by new parasite clones (termed reinfection) during the follow-up period which may share (by chance) genetic data with clones present at the time of treatment. Consequently, it is possible for drug failure to be misclassified as a reinfection or vice versa, inducing error in drug failure rate estimates. The true drug failure rate cannot be known in vivo so the accuracy of each method is not known. The results presented here show that currently used methods (length-polymorphic markers and microsatellite markers) are under-estimating true drug failure rate and preventing the detection of failing drugs (~10% failure rate). Accuracy of failure rate estimates was greatly improved by using alternative statistical algorithms or through use of novel Amplicon Sequencing techniques for genotyping blood samples. Secondly, clinical trials of severe malaria generally use reduction in circulating parasite numbers as a clinical endpoint but sequestered - not circulating - parasites are responsible for pathology in severe malaria. A mathematical model was developed to quantify the pathology of severe malaria in an in silico patient population based on sequestered parasite numbers. Results from this model then indicated that a simplified treatment regimen was generally non-inferior to the World Health Organization (WHO) recommended regimen, though specific sub-groups of patients may be at increased risk. Model results also indicated that the emergence of resistance to artesunate in parasite early ring-stages would have severe consequences for patient prognosis in cases of severe malaria.
Supervisor: Hastings, Ian ; Hodel, EvaMaria Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QU 26.5 Informatics. Automatic data processing. Computers ; QV 256 Antimalarials ; QX 135 Plasmodia ; QY 25 Laboratory techniques and procedure ; WC 750 Malaria