Grants and Contracts Details
Overview: Mathematical models for vector-borne diseases typically focus on population-level between-host transmission dynamics, with increasingly more being developed on the within-host pathogen dynamics. Fewer models have linked the between-host to the within-host dynamics. A critical gap remains to be addressed: the e.ect of within-host drug pharmacokinetics and pharmacodynamics (PK/PD) on population-level transmission, particularly their impact on the spread of drug resistant vector-borne pathogens. The PK processes of drug absorption, distribution, metabolism, and excretion determine the concentration levels of a drug in the body from time of administration. These levels determine, in part, the efficacy of a dosage regimen in clearing invading pathogens from the body (i.e. PD). Thus, the role of PK/PD in population-level disease dynamics is important for understanding the spread of drug-resistant vector-borne pathogens. Specifically, linking PK/PD in malaria transmission models allows for the study of anti-malarial drug resistance among patients within di.erent age groups. This PK-epidemiological (PK-epi) framework will incorporate di.erences that occur when an individual receives drug for preventative measures or for treatment of a symptomatic infection. In the case of malaria, Intermittent Preventative Treatment (IPT) is known to influence the selection of drug resistant parasites. Intellectual Merit: A novel model framework linking a within-human PK/PD model, whose output is drug concentration and parasite density . time units after drug administration, to a between-host model of vector-borne disease with two competing parasite strains, is presented. A two-age-structured model for malaria is constructed, in which each age group has a di.erent immune status, with only those with naive immunity undergoing IPT. The model will be used to study the impact of IPT and treatment drug PK/PD on the spread of drug-resistant malaria parasites. The three specific aims of our proposal are to: (1) build a general framework that links within-host PK/PD to between-host disease dynamics of competing pathogen strains, (2) quantify the information lost by ignoring within-host PK/PD via (i) five key metrics: the basic reproduction numbers, invasion reproduction numbers, number of deaths averted, and total morbidity; and via (ii) di.erences in the emergent dynamics, (3) produce model-guided recommendations for optimal treatment and IPT protocols, bolstered by uncertainty analyses and pharmacological data. Broader Impacts: All six mathematicians have a track record of working with women and underrepresented minority high school students, undergraduates, graduate and postdoctoral fellows. All these groups will be involved in di.erent areas of the 8 stages of the project: development of the base model framework, comparison between PK-Epi model and Epi model, parameter estimation using PK/PD data, model validation using population-level statistics, uncertainty analysis, data-driven model refinement, drug half-life and protocol optimization, and broadening the scope to other systems including the study of antimicrobial resistance. Transformative Potential: The proposed framework is based on nonlinear ordinary di.erential equations. While this approach has great advantages over a partial di.erential equation model, namely tractability and ease of simulation, the ODE model requires the approximations of piecewise continuous within-host drug concentrations and continuous parasite densities by step functions, introducing uncertainty which may propagate to the population-level disease dynamics. The group will eventually link within-host PK/PD dynamics to between-host dynamics more naturally using a system of PDEs, with the trade-o. of greater mathematical and numerical complexity, including the challenge of developing a well-posed PK-Epi PDE model. Throughout the research grant timeframe, the group will tackle the challenging problem of incorporating PK/PD data as distributions in a PDE model framework, and building methods to analyze models of this type.
|Effective start/end date||9/1/18 → 6/30/19|
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