Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach.
Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach.
复制标题
DOI：
10.1371/journal.pcbi.1005650
复制
发表时间：
2017-08
影响因子：
4.3
通讯作者：
Linderman JJ
中科院分区：
文献类型：
--
作者：
Pienaar E;Sarathy J;Prideaux B;Dietzold J;Dartois V;Kirschner DE;Linderman JJ
肉芽肿是复杂的肺部病变，是结核病（TB）的标志。了解肺肉芽肿内的抗生素动力学对改善和缩短结核病的长期治疗至关重要。三种氟喹诺酮类药物（FQs）通常作为耐多药结核病治疗的一部分：氟沙星（MXF），左氧氟沙星（LVX）或加替沙星（GFX）。到目前为止，没有足够的数据支持选择一种FQ而不是另一种，或者表明这些药物在临床上是等效的。为了预测MXF，LVX和GFX在单个肉芽肿水平的疗效，我们将计算建模与实验数据集集成到一个单一的机制框架GranSim中。GranSim是一种基于混合代理的计算模型，模拟肉芽肿形成和功能，FQ血浆和组织药代动力学和药效学，并基于广泛的体外和体内数据。我们采用每种FQ的推荐日剂量对肉芽肿进行计算机模拟治疗，并通过多个指标比较疗效：细菌载量、灭菌率、早期杀菌活性以及不依从和治疗中断情况下的疗效。GranSim再现这些FQs的体内血浆药代动力学、空间和时间组织药代动力学以及体外药效学。我们预测MXF比LVX和GFX更快地杀死细胞内细菌，部分原因是细胞积累率更高。我们还表明，所有三个FQs的斗争，以消除非复制细菌居住在干酪根。这是由于酪蛋白内的药物浓度适中，而该细菌亚群的抑制浓度较高。与GFX相比，MXF和LVX具有更高的肉芽肿灭菌率; MXF在模拟的不依从或治疗中断情况下表现更好。我们的结论是，MXF具有一个小的，但潜在的临床显着的优势比LVX，以及LVX比GFX。我们说明了如何结合实验和计算方法的系统药理学方法可以指导结核病的抗生素选择。结核病（TB）是由结核分枝杆菌（Mtb）感染引起的，每年造成150万人死亡。结核病需要至少6个月的治疗，最多使用4种药物，其特征是在患者肺部形成肉芽肿。肉芽肿是宿主细胞和细菌的球形集合。氟喹诺酮类药物（FQs）是一类可以帮助缩短结核病治疗时间的药物。用于治疗TB的三种FQs是：氟沙星（MXF），左氧氟沙星（LVX）或加替沙星（GFX）。到目前为止，还不清楚是否一种FQ在治疗结核病方面优于其他药物，部分原因是对这些药物如何在肺肉芽肿内分布和发挥作用知之甚少。我们使用计算机模拟Mtb感染和FQ治疗肉芽肿来预测哪种FQ更好以及原因。我们的计算机模型被校准到多个实验数据集。我们通过多个指标比较了三种FQs，并预测MXF优于LVX和GFX，因为它更快地杀死细菌，并且当患者错过剂量时效果更好。然而，所有三种FQs都不能杀死生活在肉芽肿中心的部分细菌种群。我们的结果现在可以为未来的实验研究提供信息。
Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB. Tuberculosis (TB) is caused by infection with the bacterium Mycobacterium tuberculosis (Mtb) and kills 1.5 million people each year. TB requires at least 6 months of treatment with up to four drugs, and is characterized by formation of granulomas in patient lungs. Granulomas are spherical collections of host cells and bacteria. Fluoroquinolones (FQs) are a class of drug that could help shorten TB treatment. Three FQs that are used to treat TB are: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, it is unclear if one FQ is better than the others at treating TB, in part because little is known about how these drugs distribute and work inside the lung granulomas. We use computer simulations of Mtb infection and FQ treatment within granulomas to predict which FQ is better and why. Our computer model is calibrated to multiple experimental data sets. We compare the three FQs by multiple metrics, and predict that MXF is better than LVX and GFX because it kills bacteria more quickly, and it works better when patients miss doses. However, all three FQs are unable to kill a part of the bacterial population living in the center of granulomas. Our results can now inform future experimental studies.
登录
查看更多内容
DOI：
10.1126/science.aad3292
发表时间：
2016-01-01
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Baym M;Stone LK;Kishony R
通讯作者：
Kishony R
影响因子：
9.6
作者：
Capitano, B;Mattoes, HM;Nicolau, DP
通讯作者：
Nicolau, DP
影响因子：
3.2
作者：
Donald, P. R.;Diacon, A. H.
通讯作者：
Diacon, A. H.
影响因子：
4.5
作者：
Cramer, Joyce A.;Roy, Anuja;Wong, Peter K.
通讯作者：
Wong, Peter K.
影响因子：
82.9
作者：
通讯作者：
--