Applications of Quantitative Sciences for Induced Blood-Stage Controlled Human Malaria Infection Studies
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Malaria is a preventable and treatable infection, yet every two minutes a child dies from the disease. As resistance to existing therapies continues to spread, it is essential to efficiently develop new therapeutics to eradicate malaria. This thesis develops a framework for the application of quantitative sciences to analyze data generated from induced blood-stage malaria (IBSM) trials for antimalarial therapies. The successful implementation of pharmacometric methods resulted in the creation of model-informed approaches for clinical trial study design and dose selection. Population pharmacokinetic and pharmacodynamic (PK/PD) models were coded to characterize the time course of drug concentrations and parasitemia profiles in patients from IBSM studies. Nonlinear mixed effects modeling and both deterministic and stochastic simulations were conducted using the computer programs NONMEM and R. Comparisons of the simulations from the PK/PD models to the observed clinical trial data were used to establish the validity of these models and the value of pharmacometric methods. This dissertation aimed to strengthen the use of the data generated from the IBSM studies and generate a quantitative link between IBSM studies and phase 2 trials. This thesis represents the first formal investigation of best practices for the PK/PD analysis of this data. In Part 1 of this dissertation, the predictability of PK/PD models which were historically used to characterize IBSM data was evaluated and a framework to evaluate the PK/PD data on a cohort-by-cohort basis was created. The results of this work demonstrated the traditional pharmacodynamic model was not predictive of phase 2 trial data. The predictive assessment prompted the hypothesis that an adaptive IBSM study design could minimize the number of patients receiving inefficacious dioses and establish a useful dose-response relationship earlier in development. Part 2 of this dissertation investigated alternative pharmacodynamic models which were more representative of the parasite biology, and showed these models were identifiable and able to characterize the parasitemia profiles of IBSM patients. Additional analyses and gaps in knowledge were identified for future work to create a mechanistic pharmacodynamic model. An adaptive study design allowed for the characterization of a dose-response relationship after administering drug to only eight patients in a single cohort, and showed that through stochastic simulations, the population PK/PD model generated from this data was able to capture the parasitemia profiles of patients in a phase 2 trial. The final part of this dissertation established an overarching strategy for model-informed drug development for malaria therapeutics. The efforts of this dissertation culminate as recommendations for the use of pharmacometric techniques for malaria therapeutics; and were submitted to the United States Food and Drug Administration with the goal of influencing the upcoming Guidance for Industry.