Department of Clinical Sciences and Administration, University of Houston College of Pharmacy, 1441 Moursund Street, Houston, TX 77030, USA.
Antimicrob Agents Chemother. 2011 Oct;55(10):4601-5. doi: 10.1128/AAC.00508-11. Epub 2011 Aug 1.
The rapid increase in the prevalence of antibiotic-resistant pathogens is a global problem that has challenged our ability to treat serious infections. Currently, clinical decisions on treatment are often based on in vitro susceptibility data. The role of the immune system in combating bacterial infections is unequivocal, but it is not well captured quantitatively. In this study, the impact of neutrophils on bacterial clearance was quantitatively assessed in a murine pneumonia model. In vitro time-growth studies were performed to determine the growth rate constants of Acinetobacter baumannii ATCC BAA 747 and Pseudomonas aeruginosa PAO1. The absolute neutrophil count in mice resulting from different cyclophosphamide preparatory regimens was determined. The dynamic change of bacterial (A. baumannii BAA 747) burden in mice with graded immunosuppression over 24 h was captured by a mathematical model. The fit to the data was satisfactory (r(2) = 0.945). The best-fit maximal kill rate (K(k)) of the bacterial population by neutrophils was 1.743 h(-1), the number of neutrophils necessary for 50% maximal killing was 190.8/μl, and the maximal population size was 1.8 × 10(9) CFU/g, respectively. Using these model parameter estimates, the model predictions were subsequently validated by the bacterial burden change of P. aeruginosa PAO1 at 24 h. A simple mathematical model was proposed to quantify the contribution of neutrophils to bacterial clearance and predict the bacterial growth/suppression in animals. Our results provide a novel framework to link in vitro and in vivo information and may be used to improve clinical treatment of bacterial infections.
抗生素耐药病原体的迅速增加是一个全球性问题，这对我们治疗严重感染的能力提出了挑战。目前，临床治疗决策通常基于体外药敏数据。免疫系统在对抗细菌感染方面的作用是明确的，但它没有被很好地定量捕捉。在这项研究中，在鼠肺炎模型中定量评估了中性粒细胞对细菌清除的作用。进行了体外时间生长研究，以确定鲍曼不动杆菌 ATCC BAA 747 和铜绿假单胞菌 PAO1 的生长率常数。确定了不同环磷酰胺预备方案导致的小鼠中性粒细胞绝对计数。通过数学模型捕捉了 24 小时内不同程度免疫抑制小鼠中细菌（鲍曼不动杆菌 BAA 747）负荷的动态变化。数据拟合良好（r²=0.945）。中性粒细胞对细菌种群的最佳拟合最大杀伤率（K(k)）为 1.743 h(-1)，达到 50%最大杀伤所需的中性粒细胞数量为 190.8/μl，最大种群大小为 1.8×10(9) CFU/g。使用这些模型参数估计值，随后通过 24 小时时铜绿假单胞菌 PAO1 的细菌负荷变化验证了模型预测。提出了一个简单的数学模型来定量评估中性粒细胞对细菌清除的贡献，并预测动物体内的细菌生长/抑制情况。我们的结果提供了一个将体外和体内信息联系起来的新框架，可用于改善细菌感染的临床治疗。
