Abstract
ABSTRACT
Tuberculosis (TB) remains a global health concern, as
Mycobacterium tuberculosis
(Mtb) currently infects a quarter of the world’s population. Though many TB patients cure infection with short-course treatment, regimens shorter than 4 months risk post-treatment relapse. This is facilitated by the presence of granulomas, spatially heterogeneous immune structures that form during infection that harbor a caseated core that sequesters Mtb in a non-replicating state and pharmacokinetically impedes antibiotics. Typically, clinical relapse is described as TB recurrence
IMPORTANCE
Incomplete treatment of TB leads to risks relapse, which may occur years later. The threat of relapse is the main reason TB treatment takes 4–9 months. Predictors of relapse are not well-defined given variability in technical definitions of relapse and nuances of study designs. Here, we use biologically based computation to simulate both clinical and experimental TB relapse studies, employing multiple diagnostic tests and relapse definitions. We find that two hypothesized types of relapse, each possibly requiring different re-treatment strategies, are simultaneously at play. Simulations suggest that relapse after a “cure” diagnosis (most often clinical studies) is caused by reactivation of non-replicating bacteria hidden from treatment within necrotic granuloma tissue or other sites, whereas experimental systems that cannot or do not test for cure post-treatment are likely to report relapse caused by incomplete sterilization of replicating Mtb. Predictions also depend on the currently unclear relationship between bacterial burden and clinical symptoms.