Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

<div><p>The rise of multi-drug resistance has decreased the effectiveness of antibiotics, which has led to increased mortality rates associated with symptomatic bacteremia, or bacterial sepsis. To combat decreasing antibiotic effectiveness, extracorporeal bacterial separation approaches have been proposed to capture and separate bacteria from blood. However, bacteremia is dynamic and involves host-pathogen interactions across various anatomical sites. We developed a mathematical model that quantitatively describes the kinetics of pathogenesis and progression of symptomatic bacteremia under various conditions, including bacterial separation therapy, to better understand disease mechanisms and quantitatively assess the biological impact of bacterial separation therapy. Model validity was tested against experimental data from published studies. This is the first multi-compartment model of symptomatic bacteremia in mammals that includes extracorporeal bacterial separation and antibiotic treatment, separately and in combination. The addition of an extracorporeal bacterial separation circuit reduced the predicted time of total bacteria clearance from the blood of an immunocompromised rodent by 49%, compared to antibiotic treatment alone. Implementation of bacterial separation therapy resulted in predicted multi-drug resistant bacterial clearance from the blood of a human in 97% less time than antibiotic treatment alone. The model also proposes a quantitative correlation between time-dependent bacterial load among tissues and bacteremia severity, analogous to the well-known ‘area under the curve’ for characterization of drug efficacy. The engineering-based mathematical model developed may be useful for informing the design of extracorporeal bacterial separation devices. This work enables the quantitative identification of the characteristics required of an extracorporeal bacteria separation device to provide biological benefit. These devices will potentially decrease the bacterial load in blood. Additionally, the devices may achieve bacterial separation rates that allow consequent acceleration of bacterial clearance in other tissues, inhibiting the progression of symptomatic bacteremia, including multi-drug resistant variations.</p></div

<i>A</i>. <i>baumannii</i> clearance (≤1 CFU/mL) time improved upon addition of 100% efficient bacterial separation.

<p><i>A</i>. <i>baumannii</i> clearance (≤1 CFU/mL) time improved upon addition of 100% efficient bacterial separation.</p

Bacterial separation (100% and 60% efficiency) improved <i>A</i>. <i>baumannii</i> clearance rates from the blood compartment.

<p>20% bacterial separation efficiency was not efficient enough to impact the overall bacterial clearance rate and resulted in the same clearance rates as antibiotic treatment alone.</p

Bacteria separation (100% efficiency) in <i>A</i>. <i>baumannii</i> human model reduced bacterial burden experienced.

<p>Bacteria separation (100% efficiency) in <i>A</i>. <i>baumannii</i> human model reduced bacterial burden experienced.</p

Bacterial time in neutropenic rodents until reaching a lethal <i>A</i>. <i>baumannii</i> concentration.

<p>The median numbers of bacteria in each compartment observed experimentally in previous literature were used as the initial conditions for these simulations[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163167#pone.0163167.ref025" target="_blank">25</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163167#pone.0163167.ref035" target="_blank">35</a>], and trajectories were generated using the parameter estimates shown in Materials and Methods.</p

Partition Coefficient Model Parameters[18].

<p>Partition Coefficient Model Parameters[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163167#pone.0163167.ref018" target="_blank">18</a>].</p

Bacteria separation in immunocompromised <i>A</i>. <i>baumannii</i> rodent model reduced bacterial burden experienced.

<p>Bacteria separation in immunocompromised <i>A</i>. <i>baumannii</i> rodent model reduced bacterial burden experienced.</p

The five-compartment kinetic model describing bacterial pathogenesis.

<p>Intratracheal instillation of a Gram-negative bacteria bolus initially occurred in the lung compartment, with initial concentration <i>L</i>_<i>0</i> (CFU mL^-1). Bacterial proliferation rates (<i>p</i>, h⁻¹), clearance rates (<i>c</i>, h⁻¹), and transport rates between compartments were included in the model schematic. The rate of bacterial transport between compartments was represented as a function of blood flow rate per compartment volume (<i>Q</i>/<i>V</i>, mL h^-1), modified by an experimentally determined partitioning coefficient (<i>x</i>, dimensionless).</p

Treatment of MDR <i>A</i>. <i>baumannii</i> human model using 100% efficient bacterial separation with antibiotic treatment.

<p>Using this combination therapy, MDR <i>A</i>. <i>baumannii</i> clearance (≤ 1 CFU/mL) from the blood compartment of the human mathematical model occurred in 1 h. The time required for MDR <i>A</i>. <i>baumannii</i> to be cleared to a negligible concentration (≤1 CFU/mL) from the blood compartment with antibiotic administration alone was 29 h.</p

Net bacterial growth rates, <i>A</i>. <i>baumannii</i> with colistin[32].

<p>Net bacterial growth rates, <i>A</i>. <i>baumannii</i> with colistin[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163167#pone.0163167.ref032" target="_blank">32</a>].</p