In silico modeling: methods and applications to trauma and sepsis

Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies

BACKGROUND

Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting.

METHODS

We sought to bridge this gap by linking preclinical data in a porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation.

RESULTS

The mathematical model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time.

CONCLUSIONS

This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

Interleukin-17 as a spatiotemporal bridge from acute to chronic inflammation: Novel insights from computational modeling

A systematic review of several acute inflammatory diseases ranging from sepsis and trauma/hemorrhagic shock to the relevant pathology of the decade, COVID-19, points to the cytokine interleukin (IL)-17A as being centrally involved in the propagation of inflammation. We summarize the role of IL-17A in acute inflammation, leveraging insights made possible by biological network analysis and novel computational methodologies aimed at defining the spatiotemporal spread of inflammation in both experimental animal models and humans. These studies implicate IL-17A in the cross-tissue spread of inflammation, a process that appears to be in part regulated through neural mechanisms. Although acute inflammatory diseases are currently considered distinct from chronic inflammatory pathologies, we suggest that chronic inflammation may represent repeated, cyclical episodes of acute inflammation driven by mechanisms involving IL-17A. Thus, insights from computational modeling of acute inflammatory diseases may improve diagnosis and treatment of chronic inflammation; in turn, therapeutics developed for chronic/autoimmune disease may be of benefit in acute inflammation. This article is categorized under: Immune System Diseases > Computational Models.

The Use of Multiplexing to Identify Cytokine and Chemokine Networks in the Immune-Inflammatory Response to Trauma

Significance: The immunoinflammatory responses that follow trauma contribute to clinical trajectory and patient outcomes. While remarkable advances have been made in trauma services and injury management, clarity on how the immune system in humans responds to trauma is lagging. Recent Advances: Multiplexing platforms have transformed our ability to analyze comprehensive immune mediator responses in human trauma. In parallel, with the establishment of large data sets, computational methods have been adapted to yield new insights based on mediator patterns. These efforts have added an important data layer to the emerging multiomic characterization of the human response to injury. Critical Issues: Outcome after trauma is greatly affected by the host immunoinflammatory response. Excessive or sustained responses can contribute to organ damage. Hence, understanding the pathophysiology behind traumatic injury is of vital importance. Future Directions: This review summarizes our work in the study of circulating immune mediators in trauma patients. Our foundational studies into dynamic patterns of inflammatory mediators represent an important contribution to the concepts and computational challenges that these large data sets present. We hope to see further integration and understanding of multiomics strategies in the field of trauma that can aid in patient endotyping and in potentially identifiying certain therapeutic targets in the future. Antioxid. Redox Signal. 35, 1393-1406.

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model's rule set. The model's internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

SARS-CoV-2/COVID-19: Evolving Reality, Global Response, Knowledge Gaps, and Opportunities

Approximately 3 billion people around the world have gone into some form of social separation to mitigate the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The uncontrolled influx of patients in need of emergency care has rapidly brought several national health systems to near-collapse with deadly consequences to those afflicted by Coronavirus Disease 2019 (COVID-19) and other critical diseases associated with COVID-19. Solid scientific evidence regarding SARS-CoV-2/COVID-19 remains scarce; there is an urgent need to expand our understanding of the SARS-CoV-2 pathophysiology to facilitate precise and targeted treatments. The capacity for rapid information dissemination has emerged as a double-edged sword; the existing gap of high-quality data is frequently filled by anecdotal reports, contradictory statements, and misinformation. This review addresses several important aspects unique to the SARS-CoV-2/COVID-19 pandemic highlighting the most relevant knowledge gaps and existing windows-of-opportunity. Specifically, focus is given on SARS-CoV-2 immunopathogenesis in the context of experimental therapies and preclinical evidence and their applicability in supporting efficacious clinical trial planning. The review discusses the existing challenges of SARS-CoV-2 diagnostics and the potential application of translational technology for epidemiological predictions, patient monitoring, and treatment decision-making in COVID-19. Furthermore, solutions for enhancing international strategies in translational research, cooperative networks, and regulatory partnerships are contemplated.

Refining rodent models of spinal cord injury

This report was produced by an Expert Working Group (EWG) consisting of UK-based researchers, veterinarians and regulators of animal experiments with specialist knowledge of the use of animal models of spinal cord injury (SCI). It aims to facilitate the implementation of the Three Rs (Replacement, Reduction and Refinement), with an emphasis on refinement. Specific animal welfare issues were identified and discussed, and practical measures proposed, with the aim of reducing animal use and suffering, reducing experimental variability, and increasing translatability within this critically important research field.

A conceptual time window-based model for the early stratification of trauma patients

Progress in the testing of therapies targeting the immune response following trauma, a leading cause of morbidity and mortality worldwide, has been slow. We propose that the design of interventional trials in trauma would benefit from a scheme or platform that could support the identification and implementation of prognostic strategies for patient stratification. Here, we propose a stratification scheme based on defined time periods or windows following the traumatic event. This 'time-window' model allows for the incorporation of prognostic variables ranging from circulating biomarkers and clinical data to patient-specific information such as gene variants to predict adverse short- or long-term outcomes. A number of circulating biomarkers, including cell injury markers and damage-associated molecular patterns (DAMPs), and inflammatory mediators have been shown to correlate with adverse outcomes after trauma. Likewise, several single nucleotide polymorphisms (SNPs) associate with complications or death in trauma patients. This review summarizes the status of our understanding of the prognostic value of these classes of variables in predicting outcomes in trauma patients. Strategies for the incorporation of these prognostic variables into schemes designed to stratify trauma patients, such as our time-window model, are also discussed.

Computational Analysis Supports IL-17A as a Central Driver of Neutrophil Extracellular Trap-Mediated Injury in Liver Ischemia Reperfusion

Hepatic ischemia reperfusion (I/R) is a clinically relevant model of acute sterile inflammation leading to a reverberating, self-sustaining inflammatory response with resultant necrosis. We hypothesized that computerized dynamic network analysis (DyNA) of 20 inflammatory mediators could help dissect the sequence of post-I/R mediator interactions that induce injury. Although the majority of measured inflammatory mediators become elevated in the first 24 h, we predicted that only a few would be secreted early in the process and serve as organizational centers of downstream intermediator complexity. In support of this hypothesis, DyNA inferred a central organizing role for IL-17A during the first 3 h of reperfusion. After that, DyNA revealed connections among almost all the inflammatory mediators, representing an ongoing cytokine storm. Blocking IL-17A immediately after reperfusion disassembled the inflammatory networks and protected the liver from injury. Disassembly of the networks was not achieved if IL-17A blockage was delayed two or more hours postreperfusion. Network disassembly was accompanied by decrease in neutrophil infiltration and neutrophil extracellular trap (NET) formation. By contrast, administration of recombinant IL-17A increased neutrophil infiltration, NET formation, and liver necrosis. The administration of DNase, a NET inhibitor, significantly reduced hepatic damage despite prior administration of IL-17A, and DNase also disassembled the inflammatory networks. In vitro, IL-17A was a potent promoter of NET formation. Therefore, computational analysis identified IL-17A's early, central organizing role in the rapid evolution of a network of inflammatory mediators that induce neutrophil infiltration and NET formation responsible for hepatic damage after liver I/R.

A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice

Bacterial lipopolysaccharide (LPS) induces an acute inflammatory response across multiple organs, primarily via Toll-like receptor 4 (TLR4). We sought to define novel aspects of the complex spatiotemporal dynamics of LPS-induced inflammation using computational modeling, with a special focus on the timing of pathological systemic spillover. An analysis of principal drivers of LPS-induced inflammation in the heart, gut, lung, liver, spleen, and kidney to assess organ-specific dynamics, as well as in the plasma (as an assessment of systemic spillover), was carried out using data on 20 protein-level inflammatory mediators measured over 0-48h in both C57BL/6 and TLR4-null mice. Using a suite of computational techniques, including a time-interval variant of Principal Component Analysis, we confirm key roles for cytokines such as tumor necrosis factor-α and interleukin-17A, define a temporal hierarchy of organ-localized inflammation, and infer the point at which organ-localized inflammation spills over systemically. Thus, by employing a systems biology approach, we obtain a novel perspective on the time- and organ-specific components in the propagation of acute systemic inflammation.

Gram-negative bacterial lipopolysaccharide (LPS) is both a central mediator of sepsis and a canonical inducer of acute inflammation via Toll-like receptor 4 (TLR4). Sepsis involves the systemic spillover of inflammation that normally remains localized in individual organs. The goal of this study was to gain insights into 1) early vs. later drivers of LPS-induced inflammation in various compartments, and 2) the systemic spillover from affected organs vs. local production of inflammatory mediators in the blood. This study involved a large number of data points on the dynamics of inflammatory mediators at the protein level, data-driven computational modeling of principal characteristics and cross-correlations, and validation of key hypotheses. In addition to verifying key mechanisms in LPS/TLR4-driven acute inflammation, this approach yielded key insights into the progression of inflammation across tissues, and also suggested the presence of TLR4-independent pathways (especially in the gut). This is, to our knowledge, the first study examining the dynamic evolution of some key inflammatory mediators and their interactions with each other in both the systemic circulation and within a number of targeted parenchymal organs in mice.

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.

Secondary brain injury: Predicting and preventing insults

Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. We, and others, have recently developed predictive models based on machine learning from continuous time series of intracranial pressure and partial brain tissue oxygenation. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In this article, we review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithms. This article is part of the Special Issue entitled "Novel Treatments for Traumatic Brain Injury".

Diagnosis and Management of Polytraumatized Patients With Severe Extremity Trauma

Multiply injured patients with severe extremity trauma are at risk of acute systemic complications and are at high risk of developing longer term orthopaedic complications including soft-tissue infection, osteomyelitis, posttraumatic osteoarthritis, and nonunion. It is becoming increasingly recognized that injury magnitude and response to injury have major jurisdiction pertaining to patient outcomes and complications. The complexities of injury and injury response that affect outcomes present opportunities to apply precision approaches to understand and quantify injury magnitude and injury response on a patient-specific basis. Here, we present novel approaches to measure injury magnitude by adopting methods that quantify both mechanical and ischemic tissue injury specific to each patient. We also present evolving computational approaches that have provided new insight into the complexities of inflammation and immunologic response to injury specific to each patient. These precision approaches are on the forefront of understanding how to stratify individualized injury and injury response in an effort to optimize titrated orthopaedic surgical interventions, which invariably involve most of the multiply injured patients. Finally, we present novel methods directed at mangled limbs with severe soft-tissue injury that comprise severely injured patients. Specifically, methods being developed to treat mangled limbs with volumetric muscle loss have the potential to improve limb outcomes and also mitigate uncompensated inflammation that occurs in these patients.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

The role of NIGMS P50 sponsored team science in our understanding of multiple organ failure

The history of the National Institute of General Medical Sciences (NIGMS) Research Centers in Peri-operative Sciences (RCIPS) is the history of clinical, translational, and basic science research into the etiology and treatment of posttraumatic multiple organ failure (MOF). Born out of the activism of trauma and burn surgeons after the Viet Nam War, the P50 trauma research centers have been a nidus of research advances in the field and the training of future academic physician-scientists in the fields of trauma, burns, sepsis, and critical illness. For over 40 years, research conducted under the aegis of this funding program has led to numerous contributions at both the bedside and at the bench. In fact, it has been this requirement for team science with a clinician-scientist working closely with basic scientists from multiple disciplines that has led the RCIPS to its unrivaled success in the field. This review will briefly highlight some of the major accomplishments of the RCIPS program since its inception, how they have both led and evolved as the field moved steadily forward, and how they are responsible for much of our current understanding of the etiology and pathology of MOF. This review is not intended to be all encompassing nor a historical reference. Rather, it serves as recognition to the foresight and support of many past and present individuals at the NIGMS and at academic institutions who have understood the cost of critical illness and MOF to the individual and to society.

Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model

OBJECTIVES

Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28-50% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary conditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation.

DESIGN

Current predictive models for sepsis use correlative methods that are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods.

RESULTS

The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days in a sweep across the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic regions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis.

CONCLUSIONS

HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish "boundaries of futility" with respect to existing investigatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. The issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the "Crisis of Reproducibility" at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time).

Systems Medicine-Complexity Within, Simplicity Without

This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human- and patient-related data, and the ensuing complexity of information.

Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure

Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.

Machine Perfusion of Porcine Livers with Oxygen-Carrying Solution Results in Reprogramming of Dynamic Inflammation Networks

Background:Ex vivo machine perfusion (MP) can better preserve organs for transplantation. We have recently reported on the first application of an MP protocol in which liver allografts were fully oxygenated, under dual pressures and subnormothermic conditions, with a new hemoglobin-based oxygen carrier (HBOC) solution specifically developed for ex vivo utilization. In those studies, MP improved organ function post-operatively and reduced inflammation in porcine livers. Herein, we sought to refine our knowledge regarding the impact of MP by defining dynamic networks of inflammation in both tissue and perfusate.

Methods: Porcine liver allografts were preserved either with MP (n = 6) or with cold static preservation (CSP; n = 6), then transplanted orthotopically after 9 h of preservation. Fourteen inflammatory mediators were measured in both tissue and perfusate during liver preservation at multiple time points, and analyzed using Dynamic Bayesian Network (DyBN) inference to define feedback interactions, as well as Dynamic Network Analysis (DyNA) to define the time-dependent development of inflammation networks.

Results: Network analyses of tissue and perfusate suggested an NLRP3 inflammasome-regulated response in both treatment groups, driven by the pro-inflammatory cytokine interleukin (IL)-18 and the anti-inflammatory mediator IL-1 receptor antagonist (IL-1RA). Both DyBN and DyNA suggested a reduced role of IL-18 and increased role of IL-1RA with MP, along with increased liver damage with CSP. DyNA also suggested divergent progression of responses over the 9 h preservation time, with CSP leading to a stable pattern of IL-18-induced liver damage and MP leading to a resolution of the pro-inflammatory response. These results were consistent with prior clinical, biochemical, and histological findings after liver transplantation.

Conclusion: Our results suggest that analysis of dynamic inflammation networks in the setting of liver preservation may identify novel diagnostic and therapeutic modalities.

Computational Analysis Supports an Early, Type 17 Cell-Associated Divergence of Blunt Trauma Survival and Mortality

OBJECTIVE

Blunt trauma patients may present with similar demographics and injury severity yet differ with regard to survival. We hypothesized that this divergence was due to different trajectories of systemic inflammation and utilized computational analyses to define these differences.

DESIGN

Retrospective clinical study and experimental study in mice.

SETTING

Level 1 trauma center and experimental laboratory.

PATIENTS

From a cohort of 493 victims of blunt trauma, we conducted a pairwise, retrospective, case-control study of patients who survived over 24 hours but ultimately died (nonsurvivors; n = 19) and patients who, after ICU admission, went on to be discharged(survivors; n = 19).

INTERVENTIONS

None in patients. Neutralizing anti-interleukin-17A antibody in mice.

MEASUREMENTS AND MAIN RESULTS

Data on systemic inflammatory mediators assessed within the first 24 hours and over 7 days were analyzed with computational modeling to infer dynamic networks of inflammation. Network density among inflammatory mediators in nonsurvivors increased in parallel with organ dysfunction scores over 7 days, suggesting the presence of early, self-sustaining, pathologic inflammation involving high-mobility group protein B1, interleukin-23, and the Th17 pathway. Survivors demonstrated a pattern commensurate with a self-resolving, predominantly lymphoid response, including higher levels of the reparative cytokine interleukin-22. Mice subjected to trauma/hemorrhage exhibited reduced organ damage when treated with anti-interleukin-17A.

CONCLUSIONS

Variable type 17 immune responses are hallmarks of organ damage, survival, and mortality after blunt trauma and suggest a lymphoid cell-based switch from self-resolving to self-sustaining inflammation.

Dynamic Profiling: Modeling the Dynamics of Inflammation and Predicting Outcomes in Traumatic Brain Injury Patients

Inflammation induced by traumatic brain injury (TBI) is complex, individual-specific, and associated with morbidity and mortality. We sought to develop dynamic, data-driven, predictive computational models of TBI-induced inflammation based on cerebrospinal fluid (CSF) biomarkers. Thirteen inflammatory mediators were determined in serial CSF samples from 27 severe TBI patients. The Glasgow Coma Scale (GCS) score quantifies the initial severity of the neurological status of the patient on a numerical scale from 3 to 15. The 6-month Glasgow Outcome Scale (GOS) score, the outcome variable, was taken as the variable to express and predict as a function of the other input variables. Data on each subject consisting of ten clinical (one-dimensional) variables, such as age, gender, and presence of infection, along with inflammatory biomarker time series were used to generate both multinomial logistic as well as probit models that predict low (poor outcome) or high (favorable outcome) levels of the GOS score. To determine if CSF inflammation biomarkers could predict TBI outcome, a logistic model for low (≤3; poor neurological outcome) or high levels (≥4; favorable neurological outcome) of the GOS score involving a full effect of the pro-inflammatory cytokine tumor necrosis factor-α and both linear and quadratic effects of the anti-inflammatory cytokine interleukin-10 was obtained. To better stratify patients as their pathology progresses over time, a technique called “Dynamic Profiling” was developed in which patients were clustered, using the spectral Laplacian and Hartigan’s k-means method, into disjoint groups at different stages. Initial clustering was based on GCS score; subsequent clustering was performed based on clinical and demographic information and then further, sequential clustering based on the levels of individual inflammatory mediators over time. These clusters assess the risk of mortality of a new patient after each inflammatory mediator reading, based on the existing information in the previous data in the cluster to which the new patient belongs at the time, in essence acting as a “virtual clinician.” Using the Dynamic Profiling method, we show examples that suggest that severe TBI patient neurological outcomes could be predicted as a function of time post-TBI using CSF inflammatory mediators.

Expectations for the methodology and translation of animal research: a survey of the general public, medical students and animal researchers in North America

To determine what are considered acceptable standards for animal research (AR) methodology and translation rate to humans, a validated survey was sent to: a) a sample of the general public, via Sampling Survey International (SSI; Canada), Amazon Mechanical Turk (AMT; USA), a Canadian city festival (CF) and a Canadian children's hospital (CH); b) a sample of medical students (two first-year classes); and c) a sample of scientists (corresponding authors and academic paediatricians). There were 1379 responses from the general public sample (SSI, n = 557; AMT, n = 590; CF, n = 195; CH, n = 102), 205/330 (62%) medical student responses, and 23/323 (7%, too few to report) scientist responses. Asked about methodological quality, most of the general public and medical student respondents expect that: AR is of high quality (e.g. anaesthesia and analgesia are monitored, even overnight, and 'humane' euthanasia, optimal statistical design, comprehensive literature review, randomisation and blinding, are performed), and costs and difficulty are not acceptable justifications for lower quality (e.g. costs of expert consultation, or more laboratory staff). Asked about their expectations of translation to humans (of toxicity, carcinogenicity, teratogenicity and treatment findings), most expect translation more than 60% of the time. If translation occurred less than 20% of the time, a minority disagreed that this would "significantly reduce your support for AR". Medical students were more supportive of AR, even if translation occurred less than 20% of the time. Expectations for AR are much higher than empirical data show to have been achieved.

Individual-specific principal component analysis of circulating inflammatory mediators predicts early organ dysfunction in trauma patients

PURPOSE

We hypothesized that early inflammation can drive, or impact, later multiple organ dysfunction syndrome (MODS), that patient-specific principal component analysis (PCA) of circulating inflammatory mediators could reveal conserved dynamic responses which would not be apparent from the unprocessed data, and that this computational approach could segregate trauma patients with regard to subsequent MODS.

METHODS

From a cohort of 472 blunt trauma survivors, 2 separate subcohorts of moderately/severely injured patients were studied. Multiple inflammatory mediators were assessed in serial blood samples in the first 24 hours postinjury. PCA of these time course data was used to derive patient-specific "inflammation barcodes," followed by hierarchical clustering to define patient subgroups. To define the generalizability of this approach, 2 different but overlapping Luminex kits were used.

RESULTS

PCA/hierarchical clustering of 24-hour Luminex data segregated the patients into 2 groups that differed significantly in their Marshall multiple organ dysfunction score on subsequent days, independently of the specific set of inflammatory mediators analyzed. Multiple inflammatory mediators and their dynamic networks were significantly different in the 2 groups in both patient cohorts, demonstrating that the groups were defined based on "core" early responses exhibit truly different dynamic inflammatory trajectories.

CONCLUSION

Identification of patient-specific "core responses" can lead to early segregation of diverse trauma patients with regard to later MODS. Hence, we suggest that a focus on dynamic inflammatory networks rather than individual biomarkers is warranted.

Impact of Injury Severity on Dynamic Inflammation Networks Following Blunt Trauma

INTRODUCTION

Clinical outcomes following trauma depend on the extent of injury and the host's response to injury, along with medical care. We hypothesized that dynamic networks of systemic inflammation manifest differently as a function of injury severity in human blunt trauma.

STUDY DESIGN

From a cohort of 472 blunt trauma survivors studied following institutional review board approval, three Injury Severity Score (ISS) subcohorts were derived after matching for age and sex: mild ISS (49 patients [33 males and 16 females, aged 42 ± 1.9 years; ISS 9.5 ± 0.4]); moderate ISS (49 patients [33 males and 16 females, aged 42 ± 1.9; ISS 19.9 ± 0.4]), and severe ISS (49 patients [33 males and 16 females, aged 42 ± 2.5 years; ISS 33 ± 1.1]). Multiple inflammatory mediators were assessed in serial blood samples. Dynamic Bayesian Network inference was utilized to infer causal relationships based on probabilistic measures.

RESULTS

Intensive care unit length of stay, total length of stay, days on mechanical ventilation, Marshall Multiple Organ Dysfunction score, prevalence of prehospital hypotension and nosocomial infection, and admission lactate and base deficit were elevated as a function of ISS. Multiple circulating inflammatory mediators were significantly elevated in severe ISS versus moderate or mild ISS over both the first 24 h and out to 7 days after injury. Dynamic Bayesian Network suggested that interleukin 6 production in severe ISS was affected by monocyte chemotactic protein 1/CCL2, monokine inducible by interferon γ (MIG)/CXCL9, and IP-10/CXCL10; by monocyte chemotactic protein 1/CCL2 and MIG/CXCL9 in moderate ISS; and by MIG/CXCL9 alone in mild ISS over 7 days after injury.

CONCLUSIONS

Injury Severity Score correlates linearly with morbidity, prevalence of infection, and early systemic inflammatory connectivity of chemokines to interleukin 6.

Temporal Patterns of Circulating Inflammation Biomarker Networks Differentiate Susceptibility to Nosocomial Infection Following Blunt Trauma in Humans

BACKGROUND

Severe traumatic injury can lead to immune dysfunction that renders trauma patients susceptible to nosocomial infections (NI) and prolonged intensive care unit (ICU) stays. We hypothesized that early circulating biomarker patterns following trauma would correlate with sustained immune dysregulation associated with NI and remote organ failure.

METHODS

In a cohort of 472 blunt trauma survivors studied over an 8-year period, 127 patients (27%) were diagnosed with NI versus 345 trauma patients without NI. To perform a pairwise, case-control study with 1:1 matching, 44 of the NI patients were compared with 44 no-NI trauma patients selected by matching patient demographics and injury characteristics. Plasma obtained upon admission and over time were assayed for 26 inflammatory mediators and analyzed for the presence of dynamic networks.

RESULTS

Significant differences in ICU length of stay (LOS), hospital LOS, and days on mechanical ventilation were observed in the NI patients versus no-NI patients. Although NI was not detected until day 7, multiple mediators were significantly elevated within the first 24 hours in patients who developed NI. Circulating inflammation biomarkers exhibited 4 distinct dynamic patterns, of which 2 clearly distinguish patients destined to develop NI from those who did not. Mediator network connectivity analysis revealed a higher, coordinated degree of activation of both innate and lymphoid pathways in the NI patients over the initial 24 hours.

CONCLUSIONS

These studies implicate unique dynamic immune responses, reflected in circulating biomarkers that differentiate patients prone to persistent critical illness and infections following injury, independent of mechanism of injury, injury severity, age, or sex.

Trauma in silico: Individual-specific mathematical models and virtual clinical populations

Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1β inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.

Reverse Engineering the Inflammatory "Clock": From Computational Modeling to Rational Resetting

Properly-regulated inflammation is central to homeostasis. Traumatic injury, hemorrhagic shock, septic shock, and other injury-related processes such as wound healing are associated with dysregulated inflammation. Like many biological processes, inflammation is a dynamic, complex system whose function, like that of an analog clock, cannot be discerned simply from a laundry list of its parts (data). The advent of multiplexed platforms for gathering biological data, while providing an unprecedented level of detailed information about the inflammatory response, has paradoxically also proven to be overwhelming. This problem is especially acute when the datasets involve time courses, since typical statistical analyses and data-driven modeling are geared towards single time points. Various groups have addressed this problem using dynamic approaches to data-driven and mechanistic computational modeling. These modeling tools can be thought of as the "gears" and "hands" of the "clock," and have led to insights regarding principal drivers, dynamic networks, feedbacks, and regulatory switches that characterize and perhaps regulate the inflammatory response. In parallel, mechanistic computational models have given an abstracted sense of how the inflammatory "clock" works, leading to in silico models of critically ill individuals and populations. Integrating data-driven and mechanistic modeling may point the way to a rational "resetting" of inflammation via model-driven precision medicine.

Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation

SIGNIFICANCE

Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models.

RECENT ADVANCES

Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies.

CRITICAL ISSUES

Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments.

FUTURE DIRECTIONS

These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets.

Involvement of hypoxia-inducible factors in the dysregulation of oxygen homeostasis in sepsis

Sepsis is a state of infection with serious systemic manifestations, and if severe enough, can be associated with multiple organ dysfunction and systemic hypotension, which can cause tissues to be hypoxic. Inflammation, as part of the multifaceted biological response to injurious stimuli, such as pathogens or damaged tissues and cells, underlies these biological processes. Prolonged and persistent inflammation, also known as chronic inflammation, results in progressive alteration in the various types of cells at the site of inflammation and is characterized by the simultaneous destruction and healing of tissue during the process. Tissue hypoxia during inflammation is not just a simple bystander process, but can considerably affect the development or attenuation of inflammation by causing the regulation of hypoxia-dependent gene expression. Indeed, the study of transcriptionally regulated tissue adaptation to hypoxia requires intense investigation to help control hypoxia-induced inflammation and organ failure. In this review, I have described the pathophysiology of sepsis with respect to oxygen metabolism and expression of hypoxia-inducible factor 1.

Mathematical Modeling of Early Cellular Innate and Adaptive Immune Responses to Ischemia/Reperfusion Injury and Solid Organ Allotransplantation

A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression.

In vivo and systems biology studies implicate IL-18 as a central mediator in chronic pain

Inflammation is associated with peripheral neuropathy, however the interplay among cytokines, chemokines, and neurons is still unclear. We hypothesized that this neuroinflammatory interaction can be defined by computational modeling based on the dynamics of protein expression in the sciatic nerve of rats subjected to chronic constriction injury. Using Dynamic Bayesian Network inference, we identified interleukin (IL)-18 as a central node associated with neuropathic pain in this animal model. Immunofluorescence supported a role for inflammasome activation and induction of IL-18 at the site of injury. Combined in vivo and in silico approaches may thus highlight novel targets in peripheral neuropathy.

Intestinal Epithelial TLR-4 Activation Is Required for the Development of Acute Lung Injury after Trauma/Hemorrhagic Shock via the Release of HMGB1 from the Gut

The mechanisms that lead to the development of remote lung injury after trauma remain unknown, although a central role for the gut in the induction of lung injury has been postulated. We hypothesized that the development of remote lung injury after trauma/hemorrhagic shock requires activation of TLR4 in the intestinal epithelium, and we sought to determine the mechanisms involved. We show that trauma/hemorrhagic shock caused lung injury in wild-type mice, but not in mice that lack TLR4 in the intestinal epithelium, confirming the importance of intestinal TLR4 activation in the process. Activation of intestinal TLR4 after trauma led to increased endoplasmic reticulum (ER) stress, enterocyte apoptosis, and the release of circulating HMGB1, whereas inhibition of ER stress attenuated apoptosis, reduced circulating HMGB1, and decreased lung injury severity. Neutralization of circulating HMGB1 led to reduced severity of lung injury after trauma, and mice that lack HMGB1 in the intestinal epithelium were protected from the development of lung injury, confirming the importance of the intestine as the source of HMGB1, whose release of HMGB1 induced a rapid protein kinase C ζ-mediated internalization of surface tight junctions in the pulmonary epithelium. Strikingly, the use of a novel small-molecule TLR4 inhibitor reduced intestinal ER stress, decreased circulating HMGB1, and preserved lung architecture after trauma. Thus, intestinal epithelial TLR4 activation leads to HMGB1 release from the gut and the development of lung injury, whereas strategies that block upstream TLR4 signaling may offer pulmonary protective strategies after trauma.

Impact of chemically-modified tetracycline 3 on intertwined physiological, biochemical, and inflammatory networks in porcine sepsis/ARDS

Sepsis can lead to multiple organ dysfunction, including the Acute Respiratory Distress Syndrome (ARDS), due to intertwined, dynamic changes in inflammation and organ physiology. We have demonstrated the efficacy of Chemically-Modified Tetracycline 3 (CMT-3) at reducing inflammation and ameliorating pathophysiology in the setting of a clinically realistic porcine model of ARDS. Here, we sought to gain insights into the derangements that characterize sepsis/ARDS and the possible impact of CMT-3 thereon, by combined experimental and computational studies. Two groups of anesthetized, ventilated pigs were subjected to experimental sepsis via placement of a peritoneal fecal clot and intestinal ischemia/reperfusion by clamping the superior mesenteric artery for 30 min. The treatment group (n = 3) received CMT-3 at 1 hour after injury (T1), while the control group (n = 3) received a placebo. Multiple inflammatory mediators, along with clinically relevant physiologic and blood chemistry variables, were measured serially until death of the animal or T48. Principal Component Analysis (PCA) and Dynamic Bayesian Network (DBN) inference were used to relate these variables. PCA revealed a separation of cardiac and pulmonary physiologic variables by principal component, and a decreased rank of oxygen index and arterial PO2/FiO2 ratio in the treatment group compared to control. DBN suggested a conserved network structure in both control and CMT-3 animals: a response driven by positive feedback between interleukin-6 and lung dysfunction. Resulting networks further suggested that in control animals, acute kidney injury, acidosis, and respiratory failure play an increased role in the response to insult compared to CMT-3 animals. These combined in vivo and in silico studies in a high fidelity, clinically applicable animal model suggest a dynamic interplay between inflammatory, physiologic, and blood chemistry variables in the setting of sepsis and ARDS that may be dramatically altered by pleiotropic interruption of inflammation by CMT-3.

Engineering control into medicine

The human body is a tightly controlled engineering miracle. However, medical training generally does not cover "control" (in the engineering sense) in physiology, pathophysiology, and therapeutics. A better understanding of how evolved controls maintain normal homeostasis is critical for understanding the failure mode of controlled systems, that is, disease. We believe that teaching and research must incorporate an understanding of the control systems in physiology and take advantage of the quantitative tools used by engineering to understand complex systems. Control systems are ubiquitous in physiology, although often unrecognized. Here we provide selected examples of the role of control in physiology (heart rate variability, immunity), pathophysiology (inflammation in sepsis), and therapeutic devices (diabetes and the artificial pancreas). We also present a high-level background to the concept of robustly controlled systems and examples of clinical insights using the controls framework.

Removal of inflammatory ascites is associated with dynamic modification of local and systemic inflammation along with prevention of acute lung injury: in vivo and in silico studies

BACKGROUND

Sepsis-induced inflammation in the gut/peritoneal compartment occurs early in sepsis and can lead to acute lung injury (ALI). We have suggested that inflammatory ascites drives the pathogenesis of ALI and that removal of ascites with an abdominal wound vacuum prevents ALI. We hypothesized that the time- and compartment-dependent changes in inflammation that determine this process can be discerned using principal component analysis (PCA) and Dynamic Bayesian Network (DBN) inference.

METHODS

To test this hypothesis, data from a previous study were analyzed using PCA and DBN. In that study, two groups of anesthetized, ventilated pigs were subjected to experimental sepsis via intestinal ischemia/reperfusion and placement of a peritoneal fecal clot. The control group (n = 6) had the abdomen opened at 12 h after injury (T12) with attachment of a passive drain. The peritoneal suction treatment (PST) group (n = 6) was treated in an identical fashion except that a vacuum was applied to the peritoneal cavity at T12 to remove ascites and maintained until T48. Multiple inflammatory mediators were measured in ascites and plasma and related to lung function (PaO2/FIO2 ratio and oxygen index) using PCA and DBN.

RESULTS

Peritoneal suction treatment prevented ALI based on lung histopathology, whereas control animals developed ALI. Principal component analysis revealed that local to the insult (i.e., ascites), primary proinflammatory cytokines play a decreased role in the overall response in the treatment group as compared with control. In both groups, multiple, nested positive feedback loops were inferred from DBN, which included interrelated roles for bacterial endotoxin, interleukin 6, transforming growth factor β1, C-reactive protein, PaO2/FIO2 ratio, and oxygen index. von Willebrand factor was an output in control, but not PST, ascites.

CONCLUSIONS

These combined in vivo and in silico studies suggest that in this clinically realistic paradigm of sepsis, endotoxin drives the inflammatory response in the ascites, interplaying with lung dysfunction in a feed-forward loop that exacerbates inflammation and leads to endothelial dysfunction, systemic spillover, and ALI; PST partially modifies this process.

Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

Inducible protein-10, a potential driver of neurally controlled interleukin-10 and morbidity in human blunt trauma

OBJECTIVE

Blunt trauma and traumatic spinal cord injury induce systemic inflammation that contributes to morbidity. Dysregulated neural control of systemic inflammation postinjury is likely exaggerated in patients with traumatic spinal cord injury. We used in silico methods to discern dynamic inflammatory networks that could distinguish systemic inflammation in traumatic spinal cord injury from blunt trauma.

DESIGN

Retrospective study.

SETTINGS

Tertiary care institution.

PATIENTS

Twenty-one severely injured thoracocervical traumatic spinal cord injury patients and matched 21 severely injured blunt trauma patients without spinal cord injury.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

Serial blood samples were obtained from days 1 to 14 postinjury. Twenty-four plasma inflammatory mediators were quantified. Statistical significance between the two groups was determined by two-way analysis of variance. Dynamic Bayesian network inference was used to suggest dynamic connectivity and central inflammatory mediators. Circulating interleukin-10 was significantly elevated in thoracocervical traumatic spinal cord injury group versus non-spinal cord injury group, whereas interleukin-1β, soluble interleukin-2 receptor-α, interleukin-4, interleukin-5, interleukin-7, interleukin-13, interleukin-17, macrophage inflammatory protein 1α and 1β, granulocyte-macrophage colony-stimulating factor, and interferon-γ were significantly reduced in traumatic spinal cord injury group versus non-spinal cord injury group. Dynamic Bayesian network suggested that post-spinal cord injury interleukin-10 is driven by inducible protein-10, whereas monocyte chemotactic protein-1 was central in non-spinal cord injury dynamic networks. In a separate validation cohorts of 356 patients without spinal cord injury and 85 traumatic spinal cord injury patients, individuals with plasma inducible protein-10 levels more than or equal to 730 pg/mL had significantly prolonged hospital and ICU stay and days on mechanical ventilator versus patients with plasma inducible protein-10 level less than 730 pg/mL.

CONCLUSION

This is the first study to compare the dynamic systemic inflammatory responses of traumatic spinal cord injury patients versus patients without spinal cord injury, suggesting a key role for inducible protein-10 in driving systemic interleukin-10 and morbidity and highlighting the potential utility of in silico tools to identify key inflammatory drivers.

Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses

The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses from IL-10 and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.

Resuscitation with lipid emulsion: dose-dependent recovery from cardiac pharmacotoxicity requires a cardiotonic effect

BACKGROUND

Recent publications have questioned the validity of the "lipid sink" theory of lipid resuscitation while others have identified sink-independent effects and posed alternative mechanisms such as hemodilution. To address these issues, the authors tested the dose-dependent response to intravenous lipid emulsion during reversal of bupivacaine-induced cardiovascular toxicity in vivo. Subsequently, the authors modeled the relative contribution of volume resuscitation, drug sequestration, inotropy and combined drug sequestration, and inotropy to this response with the use of an in silico model.

METHODS

Rats were surgically prepared to monitor cardiovascular metrics and deliver drugs. After catheterization and instrumentation, animals received a nonlethal dose of bupivacaine to produce transient cardiovascular toxicity, then were randomized to receive one of the four treatments: 30% intravenous lipid emulsion, 20% intravenous lipid emulsion, intravenous saline, or no treatment (n = 7 per condition; 28 total animals). Recovery responses were compared with the predictions of a pharmacokinetic-pharmacodynamic model parameterized using previously published laboratory data.

RESULTS

Rats treated with lipid emulsions recovered faster than did rats treated with saline or no treatment. Intravenous lipid emulsion of 30% elicited the fastest hemodynamic recovery followed in order by 20% intravenous lipid emulsion, saline, and no treatment. An increase in arterial blood pressure underlay the recovery in both lipid emulsion-treated groups. Heart rates remained depressed in all four groups throughout the observation period. Model predictions mirrored the experimental recovery, and the model that combined volume, sequestration, and inotropy predicted in vivo results most accurately.

CONCLUSION

Intravenous lipid emulsion accelerates cardiovascular recovery from bupivacaine toxicity in a dose-dependent manner, which is driven by a cardiotonic response that complements the previously reported sequestration effect.

A Systems Engineering Perspective on Homeostasis and Disease

Engineered systems are coupled networks of interacting sub-systems, whose dynamics are constrained to requirements of robustness and flexibility. They have evolved by design to optimize function in a changing environment and maintain responses within ranges. Analysis, synthesis, and design of complex supply chains aim to identify and explore the laws governing optimally integrated systems. Optimality expresses balance between conflicting objectives while resiliency results from dynamic interactions among elements. Our increasing understanding of life’s multi-scale architecture suggests that living systems share similar characteristics with much to be learned about biological complexity from engineered systems. If health reflects a dynamically stable integration of molecules, cell, tissues, and organs; disease indicates displacement compensated for and corrected by activation and combination of feedback mechanisms through interconnected networks. In this article, we draw analogies between concepts in systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.