Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

Abstract

The complexity of the systemic inflammatory response and the lack of a treatment breakthrough in the treatment of pathogenic infection demand that advanced tools be brought to bear in the treatment of severe sepsis and trauma. Systems medicine, the translational science counterpart to basic science's systems biology, is the interface at which these tools may be constructed. Rapid initial strides in improving sepsis treatment are possible through the use of phenomenological modelling and optimization tools for process understanding and device design. Higher impact, and more generalizable, treatment designs are based on mechanistic understanding developed through the use of physiologically based models, characterization of population variability, and the use of control-theoretic systems engineering concepts. In this review we introduce acute inflammation and sepsis as an example of just one area that is currently underserved by the systems medicine community, and, therefore, an area in which contributions of all types can be made.

Figure 1.

Interactions between the four components of the inflammation system. Infection (P) triggers inflammation (N). Regulatory mechanisms, conceptualized as anti-inflammation (C), are triggered almost simultaneously. Excessive inflammation results in tissue dysfunction (D), which in turn can perpetuate inflammation.

Figure 2.

At the systemic scale, systemic inflammation decreases arterial blood pressure (X1), and therefore blood flow to organs, compromising nutrient and energy availability (see text for details). Inflammation modifies local factors (X2), further compromising the microcirculation to various tissues within organs. Tissue integrity is only possible if cells maintain their tissue-specific role, such as solute transport or metabolic function, and maintain adequate turnover and structural integrity, all of which may be compromised by inflammation-related metabolites or reprioritization of energetic resources (X3). At the lowest level, cell survival is compromised by the accumulation of toxic metabolites (X4) disrupting basic metabolism and by impaired energy production or utilization (X5).

Figure 3.

Closed-loop control schematic. Process variables of interest are measured by the sensor and compared with desired values in the controller (which includes the actuation device such as a pump or valve). Input variables are adjusted to induce changes in the outputs until the outputs reach their desired setpoint values.

Figure 4.

Schematic of the proposed decision-support system. Available patient measurements and their desired values are inputs to the algorithm. Systems engineering (optimization and control) tools, in concert with the patient model, establish the recommended (sub)optimal treatment intervention for the patient (shaded). A clinician verifies that the recommendation is reasonable, and, if so, the intervention is deployed to the patient (e.g. by closing the switch, left).

Figure 5.

Compartment-based physiological model schematic (cancer case study—note the tumour compartment). Arrows between compartments are flows (typically blood, carrying drugs or cytokines of interest quantified as concentrations). Tissues are single compartments (well perfused) or subdivided into vascular and extravascular spaces (if significant transport resistance exists). Metabolic effects, such as clearance, DNA incorporation or irreversible intracellular binding are represented by diagonal arrows exiting particular (sub)compartments. RBC, red blood cell.

Figure 6.

Receding horizon (model predictive) control implementation. Minimizing the deviation of model-predicted outputs (e.g. cytokine concentrations, crosses) from the desired reference value(s) (dashed line) is accomplished by implementing input changes (e.g. haemoadsorption device flow rate, solid line). Solution schematic is for ‘time k’, which will be updated and resolved at ‘time k + 1’ (the next measurement time).

Figure 7.

Model simulations and experimental rat endotoxaemia data. IL-6 model calibration results from (a) 3 and (b) 12 mg kg⁻¹ endotoxin challenge. IL-10 model calibration results from (c) 3 and (d) 12 mg kg⁻¹ endotoxin challenge. TNF model calibration results from (e) 3 and (f) 12 mg kg⁻¹ endotoxin challenge. Model predictions versus experimental data for (g) IL-6, (h) TNF and (i) IL-10 at an endotoxin challenge level of 6 mg kg⁻¹.

Figure 8.

Twelve-hour post-intervention mortality with (dashed lines) and without (solid lines) haemoadsorption.

Figure 9.

Clearance of (a) TNF, (b) IL-6, (c) IL-1β and (d) IL-10 by the Cytosorb blood purification device. Asterisk indicates p < 0.05 between control and treated groups.