A within-host mathematical model for the treatment of active and dormant Mycobacterium tuberculosis

2013 student Alex Becker worked with Professor Andreas Handel to develop a mathematical model of drug resistance in tuberculosis.

Abstract. With the ongoing problem of Multiple Drug Resistant and Extensively Drug Resistant Tuberculosis (TB), treatment strategies are being reassessed in hopes of making therapy more effective as well as shorter. We use a compartmental within host mathematical model and a random sampling method to simulate the effect of TB treatment on active and latent bacterial populations during full course therapy. We assess conventional treatment protocols before addressing the questions posed by the TB Modeling and Analysis Consortium: what are the best first therapy regimens, replacing rifampin with rifabutin (a rifampin like drug with a longer half life), and what are the ideal pharmacokinetic parameters of a new drug that could replace rifampin, shorten TB treatment, and clear latent TB. We use our model to show that a rifabutin regimen can considerably improve treatment success, up to 99%, in a shorter period of time, with average clearance in 50 days compared to 70-90 days. Additionally, we confirm that a novel drug with a slower decay and longer efficiency curve could shorten treatment even more, with average clearance in 37 days. Our model and results add another perspective and set of predictions to TB treatment and drug combinations.

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