A simulation model of antimalarial drug resistance

Abstract

Includes bibliographical references (leaves 132-137).Malaria ranks among the world's most important tropical parasitic diseases with world prevalence figures between 350 and 550 million clinical cases per annum. [WHO, 2008a] 'Treatment and prevention of malaria places a considerable burden on struggling economies where the disease is rampant. Research in malaria does not stop as the change in response to antimalarial drug treatment requires the development of new drugs and innovation in the use of old drugs. This thesis focused on building a model of the spread of resistance to Sulfadoxine/Pyrimethamine (SP) in a setting where both SP and SP in artemisinin-based combination therapy (ACT) are the first line therapies for malaria. The model itself is suitable to any low transmission setting where antimalarial drug resistance exists but the country of choice in this modeling exercise was Mozambique. The model was calibrated using parameters specific to the malaria situation in Mozambique. This model was intended to be used to aid decision making in countries where antimalarial drug resistance exists to help prevent resistance spreading to such an extent that drugs lose their usefulness in curing malaria. The modeling technique of choice was differential equation modeling; a simulation technique that falls under the System Dynamics banner in the Operations Research armamentarium. It is a technique that allowed the modeling of stocks and flows that represent different stages or groupings in the disease process and the rate of movement between these stages respectively. The base model that was built allowed infected individuals to become infectious, to be treated with SP or ACT and to be sensitive to or fail treatment. Individuals were allowed a period of temporary immunity where they would not be reinfected until the residual SP had been eliminated from their bloodstream. The base model was then further developed to include the pharmacokinetic properties of SP where individuals were allowed to be reinfected with certain strains of infection given the level of residual drug in their bloodstream after their current infection had been cleared. The models used in this thesis were built with idea of expanding on previous models and using available data to improve parameter estimates. The model at its core is similar to the resistance model used in Koella and Antia [2003] where differential equation modeling was used to monitor a population as it became infected with a sensitive or resistant infection and then University of Cape Town recovered. The inclusion in the model of the PK component was derived from Prudhomme-O'Meara et al. [2006] where individuals could be reinfected depending on the residual drug in their bloodstream. Rather than modeling simply sensitive and resistant infections, mutations categories were used as was the case in Watkins et al. [2005] population genetics model. The use of mutation categories allowed one to use parameters specific to these categories rather than the sensitive/resistant stratification and this is particularly relevant in Mozambique where all mutation categories still exhibit some degree of sensitivity to treatment i.e. total resistance has not yet developed for any particular mutation category. The last adaptation of the model was to use gametocyte information directly to determine human infectiousness rather than through using a gametocyte switching rate (constant multiplier used to convert parasite density to gametocyte density) as was done in Pongtavompinyo [2006]. The models developed in this thesis found that the existing vector control and drug policy in Mozambique had the major effect of decreasing total prevalence of malaria by approximately 70% in the 11 year period. The distribution of Res3 (presence of DHFR triple) and Res5 (presence of DHFR triple and DHPS double) infections changed over the 11 year period with Res3 infections initially increasing and then decreasing while Res5 infections started low and increased to overtake Res3 infections. The timing of the change in this composition of infection corresponds with the introduction of ACT and thus it appears that the use of ACT prompted the increased prevalence of quintuple parasites over DHFR triple and sensitive parasites. The total number of failures decreased substantially after the introduction of ACT to 17% of its previous level. The results of the base model corresponded with the observed data from the SEACAT study in terms of the magnitude and the trends of the impact of the change to ACT policy, but underestimated the impact of the vector control strategies compared to rapid effect noted in Sharp et al. [2007]. The Scenario testing of the base model showed that vector control is an effective strategy to reduce prevalence and that it is sensitive to the time at which the control is started as it decreased prevalence very gradually. The Scenario testing of the base model also showed that the introduction of ACT in Mozambique had a greater impact on reducing prevalence and that the start time of the ACT strategy did not decrease the effect on prevalence though earlier start times decreased the total number of resistance cases. The ratio of Res5 to Res3 infections increased faster when ACT was the treatment policy than when SP was the policy. Thus higher values of this ratio are associated with ACT being the treatment strategy in place. Thus differential equation modeling is an effective modeling tool to capture the spread of disease and to test the effects of policy interventions as it allows one to assess these effects on populations and averages out individual-level intricacies to better inform policy decisions