Quantitative Impact of Neutrophils on Bacterial Clearance in a Murine Pneumonia Model

Abstract

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/l, 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.The rapid increase in the prevalence of antibiotic-resistant pathogens is a global problem that has challenged our ability to treat serious infections. To prevent further spread of multidrug resistance and returning to the preantibiotic era, it is imperative that the current approach to treatment be improved. Presently, clinical decisions on treatment are often based on in vitro susceptibility data (MICs), which only provide information about antibacterial activity at a single time point and potentially miss information on the changes in bacterial population dynamics over time. Drug selections solely based on MIC values may not always correlate to patient outcomes (11). One major reason for the discrepancy may be due to in vitro investigations neglecting the presence of the immune system in patients, which is well known to be significant.The immune system (especially neutrophils) plays an important role in combating bacterial infections. This is best exemplified by patients with neutropenia or after transplantation, who have significantly higher mortality than patients without neutropenia (8, 10, 13). However, the effect of the immune system is not well accounted for when data obtained from in vitro investigations are used to provide guidance for treatment. The overall antimicrobial effect in a patient is the sum of the antibacterial activity from drug therapy and the effect ...

Pharmacokinetic-Pharmacodynamic Modeling of Antibacterial Drugs

“…The animal models also provide an opportunity to study the time course of the antibacterial effect imposed by the host's immune system. Some recently published articles have used mathematical models to quantitatively describe the impact of immune cells on bacterial clearance (Drusano et al, 2010a(Drusano et al, , 2011aGuo et al, 2011).…”

“…Seven days after the second cyclophosphamide dose, the neutrophil count was back to normal or higher. Different cyclophosphamide dosing regimens have also been used to study the impact of various degrees of immunosuppression in the animals (Guo et al, 2011).…”

“…Another approach to quantitatively describe the impact of the immune cells on bacterial clearance has been to follow the bacterial burden in mice with various degrees of immunosuppression (Guo et al, 2011). A pneumonia model with immunocompetent mice as well as mice rendered neutropenic using three different doses of cyclophosphamide (reducing the neutrophil count approximately by 20, 70, and 90%) was applied, and the bacterial burden was followed over time (five occasions during 24 hours).…”

Vaccine Protection of Leukopenic Mice against Staphylococcus aureus Bloodstream Infection

“…Cyclophosphamide-induced leukopenia in mice has also been used for the study of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species), antibiotic-resistant bacteria most frequently associated with nosocomial infection and therapy failure (74). While most of the work in leukopenic mice focused thus far on studying the efficacy of antibiotics (75,76), progress was also documented for the development of vaccine and immunotherapeutics to prevent P. aeruginosa BSI (77)(78)(79). Nevertheless, the development of vaccines that prevent S. aureus BSI in leukopenic mice was heretofore not investigated.…”

Translational PK/PD of anti-infective therapeutics

“…The immune system was successful in reducing the bacterial load when the initial inoculum was low; at higher initial inocula, however, the immune system’s capacity was overwhelmed resulting in net bacterial growth. This impact of immune system has been described quantitatively using mathematical models where the rate of change in bacterial count is equal to the difference between the growth rate of bacteria and the kill rate contributed by the antibacterial effect of the immune system [43]: $$\begin{gathered} \frac{\mathrm{bold-italicdN}}{\mathrm{bold-italicdt}}={\mathrm{bold-italicK}}_{\mathrm{bold-italicgrowth}}\times true(1-\frac{\mathrm{bold-italicN}}{{\mathrm{bold-italicN}}_{\mathrm{bold-italicmax}}}true) \\ \times \mathit{N}-true(\frac{{\mathit{K}}_{\mathit{tr}}bold-italic\times \mathrm{bold-italicN}}{{\mathit{N}}_{50}bold+\mathrm{bold-italicN}}true)\times \mathit{N} \end{gathered}$$ Here, K ir is the maximal kill rate induced by the immune system and N 50 is the number of bacteria per g of tissue at which the immune-system mediated kill rate is half-maximal.b) Modulation of immune reactivity : An alternative strategy to quantify the contribution of the immune system towards bacterial reduction is to use animal infection models with different levels of immunosuppression [44]. A pneumonia mouse model with functional immune system was treated with different escalating doses of the immunosuppressant cyclophosphamide, thereby reducing the neutrophil counts by 20, 70 and 90%, respectively.…”