Michael Christian T, Budak Maral, Maiello Pauline, Kracinovsky Kara, Rodgers Mark, Tomko Jaime, Lin Philana Ling, Flynn JoAnne, Linderman Jennifer J, Kirschner Denise
Department of Microbiology & Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
J Theor Biol. 2025 Aug 21;611:112176. doi: 10.1016/j.jtbi.2025.112176. Epub 2025 Jun 1.
Pulmonary infection after inhalation of Mycobacterium tuberculosis (Mtb) causes tuberculosis (TB). TB presents with lung granulomas - complex spheroidal structures composed of immune cells and bacteria. Granulomas often have centralized caseum (necrotic tissue) where mycobacteria are quarantined, complicating and prolonging multi-antibiotic regimens. Determining which antibiotic regimens are optimal for reducing treatment time and toxicity is a goal of recent TB eradication campaigns. Clinical trials are expensive and challenging, making it difficult to untangle which host-pathogen interactions drive heterogeneous infection and treatment outcomes observed both within and between hosts. To determine responses to antibiotic regimens, we simulate treatments in HostSim, our whole-host mechanistic, multi-scale computational model of Mtb-infection. HostSim tracks dynamics of pulmonary Mtb-infection over molecular, cellular, tissue, organ, and whole-host scales. We create a heterogenous virtual cohort, comprising distinct hosts, for virtual clinical trials. We represent drug treatments by newly-integrating pharmacokinetics / pharmacodynamics into HostSim, simulating treatment with commonly-prescribed TB antibiotic regimens (e.g., HRZE or BPaL). Our approach allows us to identify both (1) which hosts/granulomas improve with treatment, and (2) which mechanisms influence outcome heterogeneity. By tracking experimental and clinical measurements, we virtually recreate several drug rankings from literature. We find that many methods of ranking treatment efficacy are strongly influenced by the 'definition of improvement' used and, in some cases, the detection threshold of CFU. Our work suggests that a study's reported optimal treatment may depend on its experimental design, including initial disease state and bacterial burden measures, possibly explaining seemingly-contradictory findings from prior studies.
吸入结核分枝杆菌（Mtb）后引发的肺部感染会导致结核病（TB）。结核病表现为肺部肉芽肿——由免疫细胞和细菌组成的复杂球状结构。肉芽肿通常有中央干酪样坏死物（坏死组织），其中的分枝杆菌被隔离，这使得多抗生素治疗方案变得复杂且疗程延长。确定哪种抗生素治疗方案对于缩短治疗时间和降低毒性最为理想，是近期结核病根除运动的一个目标。临床试验成本高昂且具有挑战性，因此很难厘清在宿主内部以及不同宿主之间观察到的哪种宿主 - 病原体相互作用会导致感染和治疗结果的异质性。为了确定对抗生素治疗方案的反应，我们在HostSim中模拟治疗，HostSim是我们建立的关于Mtb感染的全宿主机制、多尺度计算模型。HostSim追踪肺部Mtb感染在分子、细胞、组织、器官和全宿主尺度上的动态变化。我们创建了一个由不同宿主组成的异质虚拟队列，用于虚拟临床试验。我们通过将药代动力学/药效学新整合到HostSim中来表示药物治疗，模拟常用的结核病抗生素治疗方案（例如，HRZE或BPaL）。我们的方法使我们能够确定：（1）哪些宿主/肉芽肿在治疗后有所改善；（2）哪些机制影响结果的异质性。通过追踪实验和临床测量数据，我们虚拟重现了文献中的几种药物排名。我们发现，许多评估治疗效果的方法受到所使用的“改善定义”的强烈影响，在某些情况下，还受到菌落形成单位（CFU）检测阈值的影响。我们的研究表明，一项研究报告的最佳治疗方案可能取决于其实验设计，包括初始疾病状态和细菌载量测量，这可能解释了先前研究中看似矛盾的结果。
