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Lewis Sheiner

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Printable version

PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
ISSN 1871-6032

PAGE 26 (2017) Abstr 7332 []

Oral: Lewis Sheiner Student Session

C-02 John Diep Host-Pathogen interactions: A mechanism-based disease progression model to describe the pathogenesis of Acinetobacter baumannii pneumonia

John K. Diep (1, 2), Alan Forrest (1, 2), Wojciech Krzyzanski (2), Coen van Hasselt (3), Thomas A. Russo (2, 4), Gauri G. Rao (1, 2)

(1) UNC Eshelman School of Pharmacy, Chapel Hill, NC, USA; (2) University at Buffalo, Buffalo, NY, USA; (3) Leiden University, The Netherlands; (4) Veterans Administration Western New York Healthcare System, Buffalo, NY, USA


Pneumonia, an acute lower respiratory tract infection, is the fourth most common cause of death globally [1]. Emergence of multi-drug resistant Gram-negative pathogens, like Acinetobacter baumannii, complicates the selection and design of effective antibiotic therapy. The clinical outcome of such infections depends on the balance between the virulence of the bacteria, the host immune response, and drug effect.

Pneumonia due to A. baumannii is recognized through its lipopolysaccharide binding to the toll-like receptor-4 complex on macrophages [2, 3]. This activates NF-κB leading to production of proinflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β). These cytokines stimulate lung epithelial cells to secrete chemokines, such as cytokine-induced neutrophil chemoattractant-1 (CINC-1), resulting in the recruitment of neutrophils to the lungs to help clear infection. Prolonged proinflammatory response can cause acute lung injury, and is regulated by anti-inflammatory cytokines [4].

Current translational research primarily focuses on the interactions between antibiotics and bacteria, neglecting the host immune response. There is a critical need to integrate host-pathogen interactions into the design and optimization of antibiotic treatment regimens using a systems-based approach. The aims of this study were (i) to develop a mechanism-based model that quantitatively describes the interactions between (1) bacterial dynamics, (2) host immune response, and (3) lung injury using an immunocompetent rat pneumonia model of infection and (ii) to predict (simulate) the effects of an altered immune system on disease progression.


Pneumonia infection was introduced in Long-Evans rats via intratracheal instillation of A. baumannii strain 307-0294 [5]. Rats were challenged with 5 different initial inocula: 7.00x106, 5.76x107, 3.50x108, 4.32x108, and 7.65x109 colony forming units (cfu)/mL with 18 animals per inoculum. During the time course of infection, terminal sampling was performed at 3, 6, 24, 48, 72, and 168 h for bronchoalveolar lavage fluid (BALF) and excision of lungs. Three animals were sacrificed at each time point. The total bacterial burden (CFU) was quantified as the sum of bacteria in the lung homogenate and BALF. Host immune response in the lung was quantified by measuring IL-1β, TNF-α, CINC-1, and neutrophil counts (NC) in BALF. Lung injury was assessed by measuring albumin concentrations (ALB) in BALF due to leakage from the vasculature into the alveolar spaces [5, 6].

A mechanism-based disease progression model was developed to describe the time course of:
1) Bacterial dynamics: bacterial replication, natural death, and clearance by neutrophil response;
2) Host immune response: stimulation of proinflammatory cytokines by bacteria and stimulation of neutrophil recruitment by proinflammatory cytokines;
3) Lung injury: leakage of albumin dependent on proinflammatory cytokine expression.

The model was used to simulate disease progression from an initial inoculum of 108 cfu/mL. NC was varied from 50-100% to simulate the effects of an altered immune system on disease progression.

Model development was conducted using a pooled approach with maximum likelihood estimation. ADAPT5 [7] was used for modeling and simulation.


Model Structure
All data were co-modeled: 5 different inocula with 6 different disease progression biomarkers per inoculum, CFU, expression of IL-1β, TNF-α, and CINC-1, recruitment of NC, and leakage of ALB.

1) The net of bacterial replication and natural death were described by a first-order process; bacterial killing by NC and neutrophil signaling were by second-order.

2) IL-1β and TNF-α were described by indirect response models [8] with capacity limited stimulation by CFU ([Smax*CFU]/[SC50+CFU]) on the rate of production. CINC-1 was described by first order stimulation by TNF-α (STNF-α) with first order elimination. NC was described by an indirect response model with linear stimulation by IL-1β (SIL-1β) and CINC-1 (SCINC-1) on rate of production. Neutrophil signaling was represented by transit compartments. An anti-inflammatory biomarker (AI) was included as an unobserved variable with capacity limited stimulation by CFU that inhibits both IL-1β and TNF-α rate of production.

3) ALB was described by an indirect response model with linear stimulation by IL-1β (SIL-1β_ALB) on rate of production.

Disease progression from initial inocula of 3.50x108, 4.32x108, and 7.65x109 (high inocula) was markedly different from 7.00x106 and 5.76x107 (low inocula). Hence, the SC50 parameters acting on stimulation of IL-1β, TNF-α, and AI by CFU were different between high and low inocula. This biological relevant infection burden threshold enabled the co-modeling of the 5 inocula.

Model Results
The model described the observed data well. Time to maximum (Tmax) CFU was predicted to range from ~15-18 h for the high inocula and ~3-6 h for the low inocula. Maximal stimulation of IL-1β ranged from ~22-30 h for high inocula and ~8-12 h for low inocula, with an estimated Smax of 96.6 (5.7% SE). Maximal stimulation of TNF-α and CINC-1 ranged from ~6-10 h and ~7-11 h with Smax and STNF-α estimates of 64.1 (5.2% SE) and 5.1 h-1 (12.7% SE), respectively. Stimulation of NC peaked from ~27-35 h for high inocula and ~9-13 h for low inocula with SIL-1β and SCINC-1 estimates of 6.51x10-3 mL/pg (39.1% SE) and 4.44x10-4 mL/pg (39.3% SE). ALB peaked from ~25-30 h for high inocula and ~9-14 h for low inocula with a SIL-1β_ALB estimate of 9.76x10-4 mL/pg (5.0% SE). Remaining parameter estimates were within physiological ranges or agreed with values reported in the literature.

The simulated infection with intact immune response (100% NC) predicted a CFU Tmax of 21.0 h, with infection burden of 2.69x102 cfu/mL by 168 h. With an altered immune response of 90% and 80% NC, the predicted CFU Tmax was 23.2 and 26.4 h, with infection burden of 2.37x105 and 2.08x108 cfu/mL by 168 h, respectively. NC ≤ 70% showed no reduction in CFU. Proinflammatory cytokine expression and lung injury were more prolonged with decreasing NC.


The model provides a reasonable description of the time course of bacterial pneumonia pathogenesis and host-pathogen interactions. It captures the maximal stimulatory effects of A. baumannii on proinflammatory cytokine expression, resulting neutrophil recruitment and response, and lung injury. It also allows for simulations of disease progression in hosts with altered immune systems.

The model will be expanded with additional biomarkers to validate neutrophil signaling and anti-inflammatory components and to provide a more comprehensive description of bacterial pathogenesis. Antibiotic PK/PD will be integrated to enable the design and optimization of novel treatment regimens.

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[7]  D’Argenio DZ, Schumitzky A, Wang X. ADAPT 5 User's Guide: Pharmacokinetic/Pharmacodynamic. Systems Analysis Software. BMSR. 2009.
[8]  Sharma A, Jusko WJ, Characteristics of indirect pharmacodynamic models and applications to clinical drug responses. Br J Clin Pharmacol. 1998 Mar; 45(3): 229–239.