UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA.
University at Buffalo, State University of New York, Buffalo, New York, USA.
CPT Pharmacometrics Syst Pharmacol. 2018 Aug;7(8):507-516. doi: 10.1002/psp4.12312. Epub 2018 Aug 24.
The emergence of highly resistant bacteria is a serious threat to global public health. The host immune response is vital for clearing bacteria from the infected host; however, the current drug development paradigm does not take host-pathogen interactions into consideration. Here, we used a systems-based approach to develop a quantitative, mechanism-based disease progression model to describe bacterial dynamics, host immune response, and lung injury in an immunocompetent rat pneumonia model. Previously, Long-Evans rats were infected with Acinetobacter baumannii (A. baumannii) strain 307-0294 at five different inocula and total lung bacteria, interleukin-1beta (IL-1β), tumor necrosis factor-α (TNF-α), cytokine-induced neutrophil chemoattractant 1 (CINC-1), neutrophil counts, and albumin were quantified. Model development was conducted in ADAPT5 version 5.0.54 using a pooled approach with maximum likelihood estimation; all data were co-modeled. The final model characterized host-pathogen interactions during the natural time course of bacterial pneumonia. Parameters were estimated with good precision. Our expandable model will integrate drug effects to aid in the design of optimized antibiotic regimens.
高度耐药菌的出现对全球公共卫生构成了严重威胁。宿主免疫反应对于清除感染宿主中的细菌至关重要；然而，当前的药物开发模式并没有考虑宿主-病原体相互作用。在这里，我们使用基于系统的方法来开发一种定量的、基于机制的疾病进展模型，以描述免疫功能正常的大鼠肺炎模型中的细菌动力学、宿主免疫反应和肺损伤。此前，长耳大鼠被不同剂量的鲍曼不动杆菌（A.baumannii）菌株 307-0294 感染，并对总肺细菌、白细胞介素-1β（IL-1β）、肿瘤坏死因子-α（TNF-α）、细胞因子诱导的中性粒细胞趋化因子 1（CINC-1）、中性粒细胞计数和白蛋白进行了定量分析。模型开发是在 ADAPT5 版本 5.0.54 中使用具有最大似然估计的 pooled 方法进行的；所有数据都进行了共同建模。最终模型描述了细菌性肺炎自然病程中宿主-病原体相互作用的特征。参数估计具有良好的精度。我们的可扩展模型将整合药物作用，以帮助设计优化的抗生素治疗方案。
