Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure

Tuberculosis (TB) remains a global health concern, as <i>Mycobacterium tuberculosis</i> (Mtb) infects a quarter of the world's population. Though many TB patients sterilize infection with treatment regimens including the current standard, incomplete sterilization leads to post-treatment relapse and development of drug resistance. Two mechanisms have been hypothesized as driving relapse: <i>persistence</i>, where treatment kills all replicating Mtb, and relapse follows once non-replicating Mtb return to a replicative niche; and <i>threshold</i>, where replicating Mtb remain alive, yet below detectable levels. Relapse is often detected through a combination of clinical and bacteriological testing, often clinically described as recurrence of TB <2 years after a "cure" diagnosis, while many experimental studies examine relapse ~2-months after treatment completion. Our capacity to untangle these considerations and identify mechanisms driving relapse <i>in vivo</i> are limited. Here, we examine the impact of both threshold and persistence mechanisms on relapse post-treatment completion and post-cure diagnosis using our computational model capturing whole-host Mtb infection dynamics. Simulations show that erroneous TB-negative diagnosis post-treatment (false cure) rates are regimen-specific, specifically, the historic standard HRZE is more likely to result in false cure than the contemporary regimens RMZE or BPaL. We also identify how threshold-driven or persistence-driven relapse correlates with both pre-treatment bacterial burden and diagnostic tests used at treatment completion. Simulations show that post-cure relapse is almost exclusively persistence driven, while threshold-driven relapse is most common without a "cured" inclusion criterion. Thus, for patients with negative bacteriological diagnostic results at treatment completion, subsequent relapse may best be personalized by targeting non-replicating Mtb.