Speaker
Description
Understanding how to safely shorten antibiotic treatment for tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is a critical step towards eradicating the world's leading cause of death by single infectious pathogen. Many patients need three months of antibiotic treatment or less, though shortening the recommended treatment duration is dangerous as we cannot predict who will have recurrent TB. The first confounding factor is the substantial heterogeneity exhibited during TB, both between and within patients. Second is data paucity, as our understanding must reconcile the limited-resolution human datasets and corresponding experimental murine and non-human primate animal models. To synthesize our knowledge toward more principled predictions of TB treatment outcomes we extended HostSim, our recent whole-host model of Mtb infection and treatment. HostSim bridges datasets for antibiotic treatment of TB and the potential for post-treatment relapse via a detailed representation of within-host pharmacokinetics, pharmacodynamics, and host-immune interactions. We have now added the ability to track in silico recreations of diagnostic tests that clinically and experimentally establish disease states. Our simulations reproduce the relative efficacy of multiple TB treatments, predict regimen-specific rates of misdiagnosed cure, and articulate how experimental and clinical study design may subtly vary what mechanisms underpin post-treatment relapse.
