Experimental validation of a theoretical model of cytokine capture using a hemoadsorption device

Abstract

Sepsis, a systemic inflammatory response in the presence of an infection, is characterized by overproduction of inflammatory mediators called cytokines. Removal of these cytokines using an extracorporeal hemoadsorption device is a potential therapy for sepsis. We are developing a cytokine adsorption device (CAD) filled with microporous polymer beads and have previously published a mathematical model which predicts the time course of cytokine removal by the device. The goal of this study was to show that the model can experimentally predict the rate of cytokine capture associated with key design and operational parameters of the CAD. We spiked IL-6, IL-10, and TNF into horse serum and perfused it through an appropriately scaled-down CAD and measured the change in concentration of the cytokines over time. These data were fit to the mathematical model to determine a single model parameter, Gamma( i ), which is only a function of the cytokine-polymer interaction and the cytokine effective diffusion coefficient in the porous matrix. We compared Gamma( i ) values, which by definition should not change between experiments. Our results indicate that the Gamma( i ) value for a specific cytokine was statistically independent of all other parameters in the model, including initial cytokine concentration, flow rate, serum reservoir volume, CAD size, and bead size. Our results also indicate that competitive adsorption of cytokines and other middle-molecular weight proteins, which is neglected in the model, does not affect the rate of removal of a given cytokine. The model of cytokine capture in the CAD developed in this study will be integrated with a systems model of sepsis to simulate the progression of sepsis in humans and to develop a therapeutic CAD design and intervention protocol that improves patient outcomes in sepsis.

Figure 1

Recirculation loop used to test cytokine removal from cytokine-spiked serum flowing through the CAD.

Figure 2

IL-6 capture data and respective model fits for initial concentrations of 1000pg/ml and 5000pg/ml. The model Γ_IL-6 fits were 1.05e-4 and 1.17e-4 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.63).

Figure 3

IL-6 capture data and respective model fits for serum flow rates of 0.8ml/min and 0.08ml/min. The model Γ_IL-6 fits were 1.05e-4 and 1.77e-4 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.49).

Figure 4

IL-6 capture data and respective model fits for CADs containing 1.5g and 10g of polymer beads. The model Γ_IL-6 fits were 1.05e-4 and 7.15e-5 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.39).

Figure 5

IL-6 capture data and respective model fits for CADs containing 289.1μm and 58.4μm radius polymer beads. The model Γ_IL-6 fits were 1.05e-4 and 8.14e-5 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.56).

Figure 6

IL-6 capture data and respective model fits for PBS/BSA reservoirs with or without an additional spike of 1.5μg/ml β2-m. The model Γ_IL-6 fits were 9.59e-5 and 8.75e-5 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.29). β2-m capture data is also shown.

Figure 7

a) IL-6 capture data alone and in a three-cytokine solution. The model Γ_IL-6 fits were 1.05e-4 and 6.22e-5 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.99). b) IL-10 capture data alone and in a three-cytokine solution. The model Γ_IL-10 fits were 6.66e-5 and 3.69e-5 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.15). c) TNF capture data alone and in a three-cytokine solution. The model Γ_TNF fits were 5.85e-6 and 9.05e-6 cm²·ml·min⁻¹·g⁻¹ and were not statistically different (p=0.99).