ポスト抗生物質結核の寛解メカニズム：寛解後と治療失敗後の違い

Tuberculosis (TB) remains a global health concern, as Mycobacterium tuberculosis (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: persistence , where treatment kills all replicating Mtb, and relapse follows once non-replicating Mtb return to a replicative niche; and threshold , 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 in vivo 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.

Incomplete treatment of TB leads to antibiotic resistance and risks relapse, which may occur years later. Understanding relapse is methodologically challenging but may allow some cases to shorten the 4-month-long recommended treatment timeframe. Predictors of relapse are not well-defined given variability in technical definitions of relapse and nuances of study designs. Here, we simulate both clinical and experimental relapse studies, including multiple diagnostic tests and relapse definitions, using biologically-based computation. We find that two hypothesized types of relapses, each potentially requiring a different treatment strategy, are simultaneously at play. Simulations suggest that relapse after "cure" diagnosis (most clinical studies) is caused by reactivation of non-replicating bacteria hidden from treatment within necrotic granuloma tissue, whereas experiments that classify any live Mtb post-treatment as relapse are more likely to report relapse caused by incomplete sterilization of replicating Mtb. Predictions depend on the currently unclear relationship between bacterial burden and clinical symptoms.