The Stop TB Partnership has prioritized the development of shorter regimens as part of their global plan to eliminate TB. In pursuit of that goal, many entities have undertaken Phase II clinical trials using 2-month culture conversion as a surrogate endpoint to test the sterilizing ability of new drug regimens. However, the sequential testing of all proposed regimens is time-consuming and cost-prohibitive, so more efficient strategies for selecting regimens for testing are required. Furthermore, without prior evaluation of the costs of new therapies, there is a risk of developing a new treatment for TB that is not economically viable. Mathematical modeling can provide a solution to this problem by synthesizing data from prior human and mouse studies to generate estimates of relapse rates for new regimens that could be used in the design of clinical trials. First, a generic Markov model of TB treatment and relapse will be developed in which individuals with TB will be assigned to one of several treatment arms, then followed in monthly cycles through an interim evaluation of culture positivity at 1, 2, 3, or 4 months. They will then be assumed to complete a consolidation phase of therapy, after which they will be followed for two years for relapse. Toxicity and tolerability will be considered at each stage. We will use appropriate distributions for all relevant parameters, particularly culture conversion and relapse rates, and use probabilistic sensitivity analysis to produce point estimates of relapse rates with 95% confidence intervals that can be used to determine which regimens are worthy of further testing. This model will then be used to accomplish 3 specific aims:
Aim 1 will be to use the model to examine the potential impact of substituting moxifloxacin for isoniazid during the first two months of TB treatment on TB relapse rates, given standard and abbreviated treatment durations.
Aim 2 will be to determine the impact of high-dose rifapentine (administered during the first 2 months of TB treatment) on relapse rates and potential for reduction of the duration of TB treatment.
Aim 3 will be to model the potential effect of linezolid on the treatment of MDR-TB, paying particular attention to the tradeoff between increased effectiveness and toxicity. Costs and cost-effectiveness will be calculated in each model.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Scientist Development Award - Research & Training (K01)
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Microbiology and Infectious Diseases B Subcommittee (MID)
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Jacobs, Gail G
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Duke University
Internal Medicine/Medicine
Schools of Medicine
United States
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