THE ACUTE INFLAMMATORY RESPONSE IN DIVERSE SHOCK STATES

Characterization of differences in immune responses during bolus and continuous infusion endotoxin challenges using mathematical modelling

Endotoxin administration is commonly used to study the inflammatory response, and though traditionally given as a bolus injection, it can be administered as a continuous infusion over multiple hours. Several studies hypothesize that the latter better represents the prolonged and pronounced inflammation observed in conditions like sepsis. Yet very few experimental studies have administered endotoxin using both strategies, leaving significant gaps in determining the underlying mechanisms responsible for their differing immune responses. We used mathematical modelling to analyse cytokine data from two studies administering a 2 ng kg-1 dose of endotoxin, one as a bolus and the other as a continuous infusion over 4 h. Using our model, we simulated the dynamics of mean and subject-specific cytokine responses as well as the response to long-term endotoxin administration. Cytokine measurements revealed that the bolus injection led to significantly higher peaks for interleukin (IL)-8, while IL-10 reaches higher peaks during continuous administration. Moreover, the peak timing of all measured cytokines occurred later with continuous infusion. We identified three model parameters that significantly differed between the two administration methods. Monocyte activation of IL-10 was greater during the continuous infusion, while tumour necrosis factor α $ {\alpha} $ and IL-8 recovery rates were faster for the bolus injection. This suggests that a continuous infusion elicits a stronger, longer-lasting systemic reaction through increased stimulation of monocyte anti-inflammatory mediator production and decreased recovery of pro-inflammatory catalysts. Furthermore, the continuous infusion model exhibited prolonged inflammation with recurrent peaks resolving within 2 days during long-term (20-32 h) endotoxin administration.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Integrated computational and in vivo models reveal Key Insights into macrophage behavior during bone healing

Myeloid-derived monocyte and macrophages are key cells in the bone that contribute to remodeling and injury repair. However, their temporal polarization status and control of bone-resorbing osteoclasts and bone-forming osteoblasts responses is largely unknown. In this study, we focused on two aspects of monocyte/macrophage dynamics and polarization states over time: 1) the injury-triggered pro- and anti-inflammatory monocytes/macrophages temporal profiles, 2) the contributions of pro- versus anti-inflammatory monocytes/macrophages in coordinating healing response. Bone healing is a complex multicellular dynamic process. While traditional in vitro and in vivo experimentation may capture the behavior of select populations with high resolution, they cannot simultaneously track the behavior of multiple populations. To address this, we have used an integrated coupled ordinary differential equations (ODEs)-based framework describing multiple cellular species to in vivo bone injury data in order to identify and test various hypotheses regarding bone cell populations dynamics. Our approach allowed us to infer several biological insights including, but not limited to,: 1) anti-inflammatory macrophages are key for early osteoclast inhibition and pro-inflammatory macrophage suppression, 2) pro-inflammatory macrophages are involved in osteoclast bone resorptive activity, whereas osteoblasts promote osteoclast differentiation, 3) Pro-inflammatory monocytes/macrophages rise during two expansion waves, which can be explained by the anti-inflammatory macrophages-mediated inhibition phase between the two waves. In addition, we further tested the robustness of the mathematical model by comparing simulation results to an independent experimental dataset. Taken together, this novel comprehensive mathematical framework allowed us to identify biological mechanisms that best recapitulate bone injury data and that explain the coupled cellular population dynamics involved in the process. Furthermore, our hypothesis testing methodology could be used in other contexts to decipher mechanisms in complex multicellular processes.

Myeloid-derived monocytes/macrophages are key cells for bone remodeling and injury repair. However, their temporal polarization status and control of bone-resorbing osteoclasts and bone-forming osteoblasts responses is largely unknown. In this study, we focused on two aspects of monocyte/macrophage population dynamics: 1) the injury-triggered pro- and anti-inflammatory monocytes/macrophages temporal profiles, 2) the contributions of pro- versus anti-inflammatory monocytes/macrophages in coordinating healing response. In order to test various hypotheses regarding bone cell populations dynamics, we have integrated a coupled ordinary differential equations-based framework describing multiple cellular species to in vivo bone injury data. Our approach allowed us to infer several biological insights including: 1) anti-inflammatory macrophages are key for early osteoclast inhibition and pro-inflammatory macrophage suppression, 2) pro-inflammatory macrophages are involved in osteoclast bone resorptive activity, whereas osteoblasts promote osteoclast differentiation, 3) Pro-inflammatory monocytes/macrophages rise during two expansion waves, which can be explained by the anti-inflammatory macrophages-mediated inhibition phase between the two waves. Taken together, this mathematical framework allowed us to identify biological mechanisms that recapitulate bone injury data and that explain the coupled cellular population dynamics involved in the process.

MATHEMATICAL INSIGHTS INTO THE DYNAMICS OF INNATE IMMUNE RESPONSE DURING INFLAMMATION

Innate immune system cells activate in response to infection and trigger an acute inflammatory reaction to restore tissue homeostasis and promote subsequent tissue repair. Their activation and functions must be very well regulated to avoid tissue damage, organ dysfunction, or even death. In this work, a new set of mathematical models is presented to examine the dynamics of the innate immune system response to tissue damage and provide further understanding of the role of the innate immune system during the early stages of an inflammatory response. Different damaged cells production functions are proposed to represent the effect of secondary tissue damage by the innate immune system. The stability and bifurcation analyses of the model reveal that there is an important threshold parameter that can be controlled in order to avoid sustained chronic inflammation and secure a successful healing outcome. A set of numerical simulations is also performed to support the presented theoretical results and demonstrate the medical applicability of the new mathematical model.

Sex differences in systemic bone and muscle loss following femur fracture in mice

Fracture induces systemic bone loss in mice and humans, and a first (index) fracture increases the risk of future fracture at any skeletal site more in men than women. The etiology of this sex difference is unknown, but fracture may induces a greater systemic bone loss response in men. Also sex differences in systemic muscle loss after fracture have not been examined. We investigated sex differences in systemic bone and muscle loss after transverse femur fracture in 3-month-old male and female C57BL/6 J mice. Whole-body and regional bone mineral content and density (BMC and BMD), trabecular and cortical bone microstructure, muscle contractile force, muscle mass, and muscle fiber size were quantified at multiple time points postfracture. Serum concentrations of inflammatory cytokines (IL-1β, IL-6, and TNF-α) were measured 1-day postfracture. One day postfracture, IL-6 and Il-1B were elevated in fracture mice of both sexes, but TNF-α was only elevated in male fracture mice. Fracture reduced BMC, BMD, and trabecular bone microstructural properties in both sexes 2 weeks postfracture, but declines were greater in males. Muscle contractile force, mass, and fiber size decreased primarily in the fractured limb at 2 weeks postfracture and females showed a trend toward greater muscle loss. Bone and muscle properties recovered by 6 weeks postfracture. Overall, postfracture systemic bone loss is greater in men, which may contribute to sex differences in subsequent fracture risk. In both sexes, muscle loss is primarily confined to the injured limb and fracture may induce greater inflammation in males.

A data-driven model of the role of energy in sepsis

Exposure to pathogens elicits a complex immune response involving multiple interdependent pathways. This response may mitigate detrimental effects and restore health but, if imbalanced, can lead to negative outcomes including sepsis. This complexity and need for balance pose a challenge for clinicians and have attracted attention from modelers seeking to apply computational tools to guide therapeutic approaches. In this work, we address a shortcoming of such past efforts by incorporating the dynamics of energy production and consumption into a computational model of the acute immune response. With this addition, we performed fits of model dynamics to data obtained from non-human primates exposed to Escherichia coli. Our analysis identifies parameters that may be crucial in determining survival outcomes and also highlights energy-related factors that modulate the immune response across baseline and altered glucose conditions.

A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus

Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

A Turing mechanism in order to explain the patchy nature of Crohn's disease

Crohn's disease is an inflammatory bowel disease (IBD) that is not well understood. In particular, unlike other IBDs, the inflamed parts of the intestine compromise deep layers of the tissue and are not continuous but separated and distributed through the whole gastrointestinal tract, displaying a patchy inflammatory pattern. In the present paper, we introduce a toy-model which might explain the appearance of such patterns. We consider a reaction-diffusion system involving bacteria and phagocyte and prove that, under certain conditions, this system might reproduce an activator-inhibitor dynamic leading to the occurrence of Turing-type instabilities. In other words, we prove the existence of stable stationary solutions that are spatially periodic and do not vanish in time. We also propose a set of parameters for which the system exhibits such phenomena and compare it with realistic parameters found in the literature. This is the first time, as far as we know, that a Turing pattern is investigated in inflammatory models.

Modeling the circadian regulation of the immune system: Sexually dimorphic effects of shift work

The circadian clock exerts significance influence on the immune system and disruption of circadian rhythms has been linked to inflammatory pathologies. Shift workers often experience circadian misalignment as their irregular work schedules disrupt the natural light-dark cycle, which in turn can cause serious health problems associated with alterations in genetic expressions of clock genes. In particular, shift work is associated with impairment in immune function, and those alterations are sex-specific. The goal of this study is to better understand the mechanisms that explain the weakened immune system in shift workers. To achieve that goal, we have constructed a mathematical model of the mammalian pulmonary circadian clock coupled to an acute inflammation model in the male and female rats. Shift work was simulated by an 8h-phase advance of the circadian system with sex-specific modulation of clock genes. The model reproduces the clock gene expression in the lung and the immune response to various doses of lipopolysaccharide (LPS). Under normal conditions, our model predicts that a host is more sensitive to LPS at circadian time (CT) CT12 versus CT0 due to a dynamic change of Interleukin 10 (IL-10), an anti-inflammatory cytokine. We identify REV-ERB as a key modulator of IL-10 activity throughout the circadian day. The model also predicts a reversal of the times of lowest and highest sensitivity to LPS, with males and females exhibiting an exaggerated response to LPS at CT0, which is countered by a blunted immune response at CT12. Overall, females produce fewer pro-inflammatory cytokines than males, but the extent of sequelae experienced by males and females varies across the circadian day. This model can serve as an essential component in an integrative model that will yield mechanistic understanding of how shift work-mediated circadian disruptions affect the inflammatory and other physiological responses.

A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during an endotoxin challenge

KEY POINTS

Inflammation in response to bacterial endotoxin challenge impacts physiological functions, including cardiovascular, thermal and pain dynamics, although the mechanisms are poorly understood. We develop an innovative mathematical model incorporating interaction pathways between inflammation and physiological processes observed in response to an endotoxin challenge. We calibrate the model to individual data from 20 subjects in an experimental study of the human inflammatory and physiological responses to endotoxin, and we validate the model against human data from an independent study. Using the model to simulate patient responses to different treatment modalities reveals that a multimodal treatment combining several therapeutic strategies gives the best recovery outcome.

ABSTRACT

Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory conditions such as sepsis, one of the ten leading causes of death in the USA, and one of the three leading causes of death in the intensive care unit. Understanding how inflammation affects physiological processes, including cardiovascular, thermal and pain dynamics, can improve a patient's chance of recovery after an inflammatory event caused by surgery or a severe infection. Although the effects of the autonomic response on the inflammatory system are well-known, knowledge about the reverse interaction is lacking. The present study develops a mathematical model analyzing the inflammatory system's interactions with thermal, pain and cardiovascular dynamics in response to a bacterial endotoxin challenge. We calibrate the model with individual data from an experimental study of the inflammatory and physiological responses to a one-time administration of endotoxin in 20 healthy young men and validate it against data from an independent endotoxin study. We use simulation to explore how various treatments help patients exposed to a sustained pathological input. The treatments explored include bacterial endotoxin adsorption, antipyretics and vasopressors, as well as combinations of these. Our findings suggest that the most favourable recovery outcome is achieved by a multimodal strategy, combining all three interventions to simultaneously remove endotoxin from the body and alleviate symptoms caused by the immune system as it fights the infection.

A biomathematical model of immune response and barrier function in mice with pneumococcal lung infection

Pneumonia is one of the leading causes of death worldwide. The course of the disease is often highly dynamic with unforeseen critical deterioration within hours in a relevant proportion of patients. Besides antibiotic treatment, novel adjunctive therapies are under development. Their additive value needs to be explored in preclinical and clinical studies and corresponding therapy schedules require optimization prior to introduction into clinical practice. Biomathematical modeling of the underlying disease and therapy processes might be a useful aid to support these processes. We here propose a biomathematical model of murine immune response during infection with Streptococcus pneumoniae aiming at predicting the outcome of different treatment schedules. The model consists of a number of non-linear ordinary differential equations describing the dynamics and interactions of the pulmonal pneumococcal population and relevant cells of the innate immune response, namely alveolar- and inflammatory macrophages and neutrophils. The cytokines IL-6 and IL-10 and the chemokines CCL2, CXCL1 and CXCL5 are considered as major mediators of the immune response. We also model the invasion of peripheral blood monocytes, their differentiation into macrophages and bacterial penetration through the epithelial barrier causing blood stream infections. We impose therapy effects on this system by modelling antibiotic therapy and treatment with the novel C5a-inactivator NOX-D19. All equations are derived by translating known biological mechanisms into equations and assuming appropriate response kinetics. Unknown model parameters were determined by fitting the predictions of the model to time series data derived from mice experiments with close-meshed time series of state parameters. Parameter fittings resulted in a good agreement of model and data for the experimental scenarios. The model can be used to predict the performance of alternative schedules of combined antibiotic and NOX-D19 treatment. We conclude that we established a comprehensive biomathematical model of pneumococcal lung infection, immune response and barrier function in mice allowing simulations of new treatment schedules. We aim to validate the model on the basis of further experimental data. We also plan the inclusion of further novel therapy principles and the translation of the model to the human situation in the near future.

Lipid Mediators in Critically Ill Patients: A Step Towards Precision Medicine

A dysregulated response to systemic inflammation is a common pathophysiological feature of most conditions encountered in the intensive care unit (ICU). Recent evidence indicates that a dysregulated inflammatory response is involved in the pathogenesis of various ICU-related disorders associated with high mortality, including sepsis, acute respiratory distress syndrome, cerebral and myocardial ischemia, and acute kidney injury. Moreover, persistent or non-resolving inflammation may lead to the syndrome of persistent critical illness, characterized by acquired immunosuppression, catabolism and poor long-term functional outcomes. Despite decades of research, management of many disorders in the ICU is mostly supportive, and current therapeutic strategies often do not take into account the heterogeneity of the patient population, underlying chronic conditions, nor the individual state of the immune response. Fatty acid-derived lipid mediators are recognized as key players in the generation and resolution of inflammation, and their signature provides specific information on patients' inflammatory status and immune response. Lipidomics is increasingly recognized as a powerful tool to assess lipid metabolism and the interaction between metabolic changes and the immune system via profiling lipid mediators in clinical studies. Within the concept of precision medicine, understanding and characterizing the individual immune response may allow for better stratification of critically ill patients as well as identification of diagnostic and prognostic biomarkers. In this review, we provide an overview of the role of fatty acid-derived lipid mediators as endogenous regulators of the inflammatory, anti-inflammatory and pro-resolving response and future directions for use of clinical lipidomics to identify lipid mediators as diagnostic and prognostic markers in critical illness.

BurnCare tablet trainer to enhance burn injury care and treatment

BACKGROUND

Applied Research Associates (ARA) and the United States Army Institute of Surgical Research (USAISR) have been developing a tablet-based simulation environment for burn wound assessment and burn shock resuscitation. This application aims to supplement the current gold standard in burn care education, the Advanced Burn Life Support (ABLS) curriculum.

RESULTS

Subject matter experts validate total body surface area (TBSA) identification and analysis and show that the visual fidelity of the tablet virtual patients is consistent with real life thermal injuries. We show this by noting that the error between their burn mapping and the actual patient burns was sufficiently less than that of a random sample population. Statistical analysis is used to confirm this hypothesis. In addition a full body physiology model developed for this project is detailed. Physiological results, and responses to standard care treatment, are detailed and validated. Future updates will include training modules that leverage this model.

CONCLUSION

We have created an accurate, whole-body model of burn TBSA training experience in Unreal 4 on a mobile platform, provided for free to the medical community. We hope to provide learners with more a realistic experience and with rapid feedback as they practice patient assessment, intervention, and reassessment.

Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We examine the temporal evolution of patient state using our previously-published method for computing risk of transition from sepsis into septic shock. Risk trajectories diverge into four clusters following early prediction of septic shock, stratifying by outcome: the highest-risk and lowest-risk groups have a 76.5% and 10.4% prevalence of septic shock, and 43% and 18% mortality, respectively. These clusters differ also in treatments received and median time to shock onset. Analyses reveal the existence of a rapid (30–60 min) transition in risk at the time of threshold crossing. We hypothesize that this transition occurs as a result of the failure of compensatory biological systems to cope with infection, resulting in a bifurcation of low to high risk. Such a collapse, we believe, represents the true onset of septic shock. Thus, this rapid elevation in risk represents a potential new data-driven definition of septic shock.

Dynamics of cytokines, immune cell counts and disease severity in patients with community-acquired pneumonia - Unravelling potential causal relationships

BACKGROUND

Community acquired pneumonia (CAP) is a severe and often rapidly deteriorating disease. To better understand its dynamics and potential causal relationships, we analyzed time series data of cytokines, blood and clinical parameters in hospitalized CAP patients.

METHODS

Time series data of 10 circulating cytokines, blood counts and clinical parameters were related to baseline characteristics of 403 CAP patients using univariate mixed models. Bivariate mixed models were applied to analyze correlations between the time series. To identify potential causal relationships, we inferred cross-lagged relationships between pairs of parameters using latent curve models with structured residuals.

RESULTS

IL-6 levels decreased faster over time in younger patients (P_adj = 0.06). IL-8, VCAM-1, and IL-6 correlated strongly with disease severity as assessed by the sequential organ failure assessment (SOFA) score (r = 0.49, 0.48, 0.46, respectively; all P_adj < 0.001). IL-6 and bilirubin correlated with respect to their mean levels and slopes over time (r = 0.36 and r = 0.46, respectively; P_adj < 0.001). A number of potential causal relationships were identified, e.g., a negative effect of ICAM-1 on MCP-1, or a positive effect of the level of creatinine on the subsequent VCAM-1 concentration (P < 0.001).

CONCLUSIONS

These results suggest that IL-6 trajectories of CAP patients are associated with age and run parallel to bilirubin levels. The time series analysis also unraveled directed, potentially causal relationships between cytokines, blood parameters and clinical outcomes. This will facilitate the development of mechanistic models of CAP, and with it, improvements in treatment or surveillance strategies for this disease.

TRIAL REGISTRATION

clinicaltrials.gov NCT02782013, May 25, 2016, retrospectively registered.

The Metabolic Basis of Immune Dysfunction Following Sepsis and Trauma

Critically ill, severely injured and high-risk surgical patients are vulnerable to secondary infections during hospitalization and after hospital discharge. Studies show that the mitochondrial function and oxidative metabolism of monocytes and macrophages are impaired during sepsis. Alternatively, treatment with microbe-derived ligands, such as monophosphoryl lipid A (MPLA), peptidoglycan, or β-glucan, that interact with toll-like receptors and other pattern recognition receptors on leukocytes induces a state of innate immune memory that confers broad-spectrum resistance to infection with common hospital-acquired pathogens. Priming of macrophages with MPLA, CPG oligodeoxynucleotides (CpG ODN), or β-glucan induces a macrophage metabolic phenotype characterized by mitochondrial biogenesis and increased oxidative metabolism in parallel with increased glycolysis, cell size and granularity, augmented phagocytosis, heightened respiratory burst functions, and more effective killing of microbes. The mitochondrion is a bioenergetic organelle that not only contributes to energy supply, biosynthesis, and cellular redox functions but serves as a platform for regulating innate immunological functions such as production of reactive oxygen species (ROS) and regulatory intermediates. This review will define current knowledge of leukocyte metabolic dysfunction during and after sepsis and trauma. We will further discuss therapeutic strategies that target leukocyte mitochondrial function and might have value in preventing or reversing sepsis- and trauma-induced immune dysfunction.

Complex Stability and an Irrevertible Transition Reverted by Peptide and Fibroblasts in a Dynamic Model of Innate Immunity

We here apply a control analysis and various types of stability analysis to an in silico model of innate immunity that addresses the management of inflammation by a therapeutic peptide. Motivation is the observation, both in silico and in experiments, that this therapy is not robust. Our modeling results demonstrate how (1) the biological phenomena of acute and chronic modes of inflammation may reflect an inherently complex bistability with an irrevertible flip between the two modes, (2) the chronic mode of the model has stable, sometimes unique, steady states, while its acute-mode steady states are stable but not unique, (3) as witnessed by TNF levels, acute inflammation is controlled by multiple processes, whereas its chronic-mode inflammation is only controlled by TNF synthesis and washout, (4) only when the antigen load is close to the acute mode's flipping point, many processes impact very strongly on cells and cytokines, (5) there is no antigen exposure level below which reduction of the antigen load alone initiates a flip back to the acute mode, and (6) adding healthy fibroblasts makes the transition from acute to chronic inflammation revertible, although (7) there is a window of antigen load where such a therapy cannot be effective. This suggests that triple therapies may be essential to overcome chronic inflammation. These may comprise (1) anti-immunoglobulin light chain peptides, (2) a temporarily reduced antigen load, and (3a) fibroblast repopulation or (3b) stem cell strategies.

A Full-Body Model of Burn Pathophysiology and Treatment Using the BioGears Engine

We have created a model of systemic burn pathophysiology by incorporating a mathematical model of acute inflammation within the BioGears Engine. This model produces outputs consistent with burns of varying severities and leverages existing BioGears functionality to simulate the effect of treatment on virtual patient outcome. The model performs well for standard resuscitation scenarios and we thus expect it to be useful for educational and training purposes.

Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia

Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagnosis of CAP from four clinical studies (PROGRESS, MAXSEP, SISPCT, VISEP). We categorized CAP severity into five disease states and estimated transition probabilities for CAP progression between these states and corresponding sojourn times. Good agreement between model predictions and clinical data was observed. Time courses of mortality were correctly predicted for up to 28 days, including validation with patient data not used for model calibration. We conclude that CAP disease course follows a Markov process, suggesting the necessity of daily monitoring and re-evaluation of patient's risk. Our model can be used for regular updates of risk assessments of patients and could improve the design of clinical trials by estimating transition rates for different risk groups.

A Whole-Body Mathematical Model of Sepsis Progression and Treatment Designed in the BioGears Physiology Engine

Sepsis is a debilitating condition associated with a high mortality rate that greatly strains hospital resources. Though advances have been made in improving sepsis diagnosis and treatment, our understanding of the disease is far from complete. Mathematical modeling of sepsis has the potential to explore underlying biological mechanisms and patient phenotypes that contribute to variability in septic patient outcomes. We developed a comprehensive, whole-body mathematical model of sepsis pathophysiology using the BioGears Engine, a robust open-source virtual human modeling project. We describe the development of a sepsis model and the physiologic response within the BioGears framework. We then define and simulate scenarios that compare sepsis treatment regimens. As such, we demonstrate the utility of this model as a tool to augment sepsis research and as a training platform to educate medical staff.

Induction of regional chemokine expression in response to human umbilical cord blood cell infusion in the neonatal mouse ischemia-reperfusion brain injury model

Regenerative medicine using umbilical cord blood (UCB) cells shows promise for the treatment of cerebral palsy. Although the efficacy of this therapy has been seen in the clinic, the mechanisms by which UCB cells interact and aid in the improvement of symptoms are not clear. We explored the chemokine expression profile in damaged brain tissue in the neonatal mouse ischemia-reperfusion (IR) brain injury model that was infused with human UCB (hUCB) cells. IR brain injury was induced in 9-day-old NOD/SCID mice. hUCB cells were administered 3 weeks post brain injury. Chemokine expression profiles in the brain extract were determined at various time points. Inflammatory chemokines such as CCL1, CCL17, and CXCL12 were transiently upregulated by 24 hours post brain injury. Upregulation of other chemokines, including CCL5, CCL9, and CXCL1 were prolonged up to 3 weeks post brain injury, but most chemokines dissipated over time. There were marked increases in levels of CCL2, CCL12, CCL20, and CX3CL1 in response to hUCB cell treatment, which might be related to the new recruitment and differentiation of neural stem cells, leading to the induction of tissue regeneration. We propose that the chemokine expression profile in the brain shifted from responding to tissue damage to inducing tissue regeneration. hUCB cell administration further enhanced the production of chemokines, and chemokine networks may play an active role in tissue regeneration in neonatal hypoxic-ischemic brain injury.

Alleviation of exhaustion-induced immunosuppression and sepsis by immune checkpoint blockers sequentially administered with antibiotics-analysis of a new mathematical model

Background

Sepsis-associated immune dysregulation, involving hyper-inflammation and immunosuppression, is common in intensive care patients, often leading to multiple organ dysfunction and death. The aim of this study was to identify the main driving force underlying immunosuppression in sepsis, and to suggest new therapeutic avenues for controlling this immune impairment and alleviating excessive pathogen load.

Methods

We developed two minimalistic (skeletal) mathematical models of pathogen-associated inflammation, which focus on the dynamics of myeloid, lymphocyte, and pathogen numbers in blood. Both models rely on the assumption that the presence of the pathogen causes a bias in hematopoietic stem cell differentiation toward the myeloid developmental line. Also in one of the models, we assumed that continuous exposure to pathogens induces lymphocyte exhaustion. In addition, we also created therapy models, both by antibiotics and by immunotherapy with PD-1/PD-L1 checkpoint inhibitors. Assuming realistic parameter ranges, we simulated the pathogen-associated inflammation models in silico with or without various antibiotic and immunotherapy schedules.

Results

Computer simulations of the two models show that the assumption of lymphocyte exhaustion is a prerequisite for attaining sepsis-associated immunosuppression, and that the ability of the innate and adaptive immune systems to control infections depends on the pathogen’s replication rate. Simulation results further show that combining antibiotics with immune checkpoint blockers can suffice for defeating even an aggressive pathogen within a relatively short period. This is so as long as the drugs are administered soon after diagnosis. In contrast, when applied as monotherapies, antibiotics or immune checkpoint blockers fall short of eliminating aggressive pathogens in reasonable time.

Conclusions

Our results suggest that lymphocyte exhaustion crucially drives immunosuppression in sepsis, and that one can efficiently resolve both immunosuppression and pathogenesis by timely coupling of antibiotics with an immune checkpoint blocker, but not by either one of these two treatment modalities alone. Following experimental validation, our model can be adapted to explore the potential of other therapeutic options in this field.

Electronic supplementary material

The online version of this article (10.1186/s40635-019-0260-3) contains supplementary material, which is available to authorized users.

Mathematical modeling of energy consumption in the acute inflammatory response

When a pathogen invades the body, an acute inflammatory response is activated to eliminate the intruder. In some patients, runaway activation of the immune system may lead to collateral tissue damage and, in the extreme, organ failure and death. Experimental studies have found an association between severe infections and depletion in levels of adenosine triphosphate (ATP), increase in nitric oxide production, and accumulation of lactate, suggesting that tissue energetics is compromised. In this work we present a differential equations model that incorporates the dynamics of ATP, nitric oxide, and lactate accompanying an acute inflammatory response and employ this model to explore their roles in shaping this response. The bifurcation diagram of the model system with respect to the pathogen growth rate reveals three equilibrium states characterizing the health, aseptic and septic conditions. We explore the domains of attraction of these states to inform the instantiation of heterogeneous virtual patient populations utilized in a survival analysis. We then apply the model to study alterations in the inflammatory response and survival outcomes in metabolically altered conditions such as hypoglycemia, hyperglycemia, and hypoxia.

Bridging blood cancers and inflammation: The reduced Cancitis model

A novel mechanism-based model - the Cancitis model - describing the interaction of blood cancer and the inflammatory system is proposed, analyzed and validated. The immune response is divided into two components, one where the elimination rate of malignant stem cells is independent of the level of the blood cancer and one where the elimination rate depends on the level of the blood cancer. A dimensional analysis shows that the full 6-dimensional system of nonlinear ordinary differential equations may be reduced to a 2-dimensional system - the reduced Cancitis model - using Fenichel theory. The original 18 parameters appear in the reduced model in 8 groups of parameters. The reduced model is analyzed. Especially the steady states and their dependence on the exogenous inflammatory stimuli are analyzed. A semi-analytic investigation reveals the stability properties of the steady states. Finally, positivity of the system and the existence of an attracting trapping region in the positive octahedron guaranteeing global existence and uniqueness of solutions are proved. The possible topologies of the dynamical system are completely determined as having a Janus structure, where two qualitatively different topologies appear for different sets of parameters. To classify this Janus structure we propose a novel concept in blood cancer - a reproduction ratio R. It determines the topological structure depending on whether it is larger or smaller than a threshold value. Furthermore, it follows that inflammation, affected by the exogenous inflammatory stimulation, may determine the onset and development of blood cancers. The body may manage initial blood cancer as long as the self-renewal rate is not too high, but fails to manage it if an inflammation appears. Thus, inflammation may trigger and drive blood cancers. Finally, the mathematical analysis suggests novel treatment strategies and it is used to discuss the in silico effect of existing treatment, e.g. interferon-α or T-cell therapy, and the impact of malignant cells becoming resistant.

Inflammation and Disease: Modelling and Modulation of the Inflammatory Response to Alleviate Critical Illness

Critical illness, a constellation of interrelated inflammatory and physiological derangements occurring subsequent to severe infection or injury, affects a large number of individuals in both developed and developing countries. The prototypical complex system embodied in critical illness has largely defied therapy beyond supportive care. We have focused on the utility of data-driven and mechanistic computational modelling to help address the complexity of critical illness and provide pathways towards discovering potential therapeutic options and combinations. Herein, we review recent progress in this field, with a focus on both animal and computational models of critical illness. We suggest that therapy for critical illness can be posed as a model-based dynamic control problem, and discuss novel theoretical and experimental approaches involving biohybrid devices aimed at reprogramming inflammation dynamically. Together, these advances offer the potential for Model-based Precision Medicine for critical illness.

The severity of LPS induced inflammatory injury is negatively associated with the functional liver mass after LPS injection in rat model

Background

High levels of serum lipopolysaccharide (LPS) were observed in sepsis patients with liver injury and high mortality. However, the role of liver in modulation LPS induced inflammatory injury was ill investigated. In the present study, the severity of LPS induced inflammatory response was observed after liver resection or portal branch occlusion to decreasing functional liver mass. The local and systemic damage was observed to investigate the role of liver in modulation inflammatory injury.

Methods

First, 30%, 70%, and 90% partial hepatectomy (PH) were performed, and serum TNF-α, survival rate, and hepatic LPS uptake was observed. Second, LPS-exposure of the functional liver mass was decreased by selectively blocking the RL prior to LPS-injection, which was given 30 min before a 70% PH, and the inflammatory response was compared in the occluded and the non-occluded liver. The control group was subjected to LPS injection 30 min prior to liver resection without blocking the RL transiently. The serum TNF-α, ALT, AST, creatinine levels, and urea levels, survival rate, hepatic LPS uptake, and hepatic inflammatory cytokines was observed.

Results

The decreasing of functional liver mass after 90%, 70%, and 30% PH was associated with decreased serum TNF-α, survival rate, and increased hepatic LPS uptake after LPS injection. Occluding the right lobes (RL) prior to LPS administration reversed the liver injury caused by 70% PH, indicated by 100% survival rate and decreased liver and kidney injury, and systemic inflammatory response. The induction of inflammatory response in occluding liver lobes were lower than un-occluding liver lobes.

Conclusions

The severity of the LPS-induced systemic inflammatory injury is determined by functional liver volume. This observation suggests that the liver is the central organ for the initiation of the inflammatory response, and is involved in causing a severe SIRS with systemic damage and death.

Electronic supplementary material

The online version of this article (10.1186/s12950-018-0197-4) contains supplementary material, which is available to authorized users.

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.

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.

Trauma and Endothelial Glycocalyx: The Microcirculation Helmet?

Trauma represents a remarkable social and economical burden, being a leading cause of death and morbidity in the young population. The Endothelial Glycocalyx (EG) is a web of membrane bound to the luminal side of the blood vessels endothelium. Its role includes maintenance of the vascular permeability barrier and mediation of shear response. The contribution of the EG to a number of clinical conditions, sepsis, and ischemia/reperfusion injury among others has been well studied. With this review we initially explore the role of the EG in the microcirculatory dysfunction associated with trauma. Subsequently, we investigate the impact of fluid administration on the EG, including its potential of protecting the microcirculation from the detrimental effects of trauma. Particular emphasis is reserved to the role of inflammatory modulation and sensible fluid resuscitation.

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Dose-dependent effects of peroxisome proliferator-activated receptors β/δ agonist on systemic inflammation after haemorrhagic shock

INTRODUCTION

PPARβ/δ agonists are known to modulate the systemic inflammatory response after sepsis. In this study, inflammation modulation effects of PPARβ/δ are investigated using the selective PPARβ/δ agonist (GW0742) in a model of haemorrhagic shock (HS)-induced sterile systemic inflammation.

METHODS

Blood pressure-controlled (35±5mmHg) HS was performed in C57/BL6 mice for 90min. Low-dose GW0742 (0.03mg/kg/BW) and high-dose GW0742 (0.3mg/kg/BW) were then administered at the beginning of resuscitation. Mice were sacrificed 6h after induction of HS. Plasma levels of IL-6, IL-1β, IL-10, TNFα, KC, MCP-1, and GM-CSF were determined by ELISA. Myeloperoxidase (MPO) activity in pulmonary and liver tissues was analysed with standardised MPO kits.

RESULTS

In mice treated with high-dose GW0742, plasma levels of IL-6, IL-1β, and MCP-1 were significantly increased compared to the control group mice. When compared to mice treated with low-dose GW0742 plasma levels of IL-6, IL-1β, GM-CSF, KC, and MCP-1 were significantly elevated in high-dose-treated mice. Low-dose GW0742 treatment was associated with a non-significant downtrend of inflammatory factors in mice with HS. No significant changes of MPO activity in lung and liver were observed between the control group and the GW0742 treatment groups.

CONCLUSION

This study identified dose-dependent effects of GW0742 on systemic inflammation after HS. While high-dose GW0742 substantially enhanced the systemic inflammatory response, low-dose GW0742 led to a downtrend of pro-inflammation cytokine expression. The exact mechanisms are yet unknown and need to be assessed in further studies.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Modeling and Hemofiltration Treatment of Acute Inflammation

The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential filtering of cytokines and WBC does not provide substantial improvement in treatment outcomes vs. existing HA devices.

Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

A Biomathematical Model of Pneumococcal Lung Infection and Antibiotic Treatment in Mice

Pneumonia is considered to be one of the leading causes of death worldwide. The outcome depends on both, proper antibiotic treatment and the effectivity of the immune response of the host. However, due to the complexity of the immunologic cascade initiated during infection, the latter cannot be predicted easily. We construct a biomathematical model of the murine immune response during infection with pneumococcus aiming at predicting the outcome of antibiotic treatment. The model consists of a number of non-linear ordinary differential equations describing dynamics of pneumococcal population, the inflammatory cytokine IL-6, neutrophils and macrophages fighting the infection and destruction of alveolar tissue due to pneumococcus. Equations were derived by translating known biological mechanisms and assuming certain response kinetics. Antibiotic therapy is modelled by a transient depletion of bacteria. Unknown model parameters were determined by fitting the predictions of the model to data sets derived from mice experiments of pneumococcal lung infection with and without antibiotic treatment. Time series of pneumococcal population, debris, neutrophils, activated epithelial cells, macrophages, monocytes and IL-6 serum concentrations were available for this purpose. The antibiotics Ampicillin and Moxifloxacin were considered. Parameter fittings resulted in a good agreement of model and data for all experimental scenarios. Identifiability of parameters is also estimated. The model can be used to predict the performance of alternative schedules of antibiotic treatment. We conclude that we established a biomathematical model of pneumococcal lung infection in mice allowing predictions regarding the outcome of different schedules of antibiotic treatment. We aim at translating the model to the human situation in the near future.

Effectiveness of Multiple Blood-Cleansing Interventions in Sepsis, Characterized in Rats

Sepsis is a serious, life-threatening condition that presents a growing problem in medicine, but there is still no satisfying solution for treating it. Several blood cleansing approaches recently gained attention as promising interventions that target the main site of problem development–the blood. The focus of this study is an evaluation of the theoretical effectiveness of hemoadsorption therapy and pathogen reduction therapy. This is evaluated using the mathematical model of Murine sepsis, and the results of over 2,200 configurations of single and multiple intervention therapies simulated on 5,000 virtual subjects suggest the advantage of pathogen reduction over hemoadsorption therapy. However, a combination of two approaches is found to take advantage of their complementary effects and outperform either therapy alone. The conducted computational experiments provide unprecedented evidence that the combination of two therapies synergistically enhances the positive effects beyond the simple superposition of the benefits of two approaches. Such a characteristic could have a profound influence on the way sepsis treatment is conducted.

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.

Prehospital use of plasma in traumatic hemorrhage (The PUPTH Trial): study protocol for a randomised controlled trial

Background

Severe traumatic injury and haemorrhagic shock are frequently associated with disruptions of coagulation function (such as trauma-induced coagulopathy TIC) and activation of inflammatory cascades. These pathologies may be exacerbated by current standard of care resuscitation protocols. Observational studies suggest early administration of plasma to severely-injured haemorrhaging patients may correct TIC, minimise inflammation, and improve survival. The proposed randomised clinical trial will evaluate the clinical effectiveness of pre-hospital plasma administration compared with standard- of-care crystalloid resuscitation in severely-injured patients with major traumatic haemorrhage.

Methods/design

This is a prospective, randomized, open-label, non-blinded trial to determine the effect of pre-hospital administration of thawed plasma (TP) on mortality, morbidity, transfusion requirements, coagulation, and inflammatory response in severely-injured bleeding trauma patients. Two hundred and ten eligible adult trauma patients will be randomised to receive either two units of plasma, to be administered in-field, vs standard of care normal saline (NS). Main analyses will compare subjects allocated to TP to those allocated to NS, on an intention-to-treat basis. Primary outcome measure is all-cause 30-day mortality. Secondary outcome measures include coagulation and lipidomic/pro-inflammatory marker responses, volume of resuscitation fluids (crystalloid, colloid) and blood products administered, and major hospital outcomes (e.g. incidence of MSOF, length of ICU stay, length of hospital stay).

Discussion

This study is part of a US Department of Defense (DoD)-funded multi-institutional investigation, conducted independently of, but in parallel with, the University of Pittsburgh and University of Denver. Demonstration of significant reductions in mortality and coagulopathic/inflammatory-related morbidities as a result of pre-hospital plasma administration would be of considerable clinical importance for the management of haemorrhagic shock in both civilian and military populations.

Trial registration

ClinicalTrials.gov: NCT02303964 on 28 November 2014

Inflammatory response to trauma: implications for coagulation and resuscitation

PURPOSE OF REVIEW

Recent studies have changed our understanding of the timing and interactions of the inflammatory processes and coagulation cascade following severe trauma. This review highlights this information and correlates its impact on the current clinical approach for fluid resuscitation and treatment of coagulopathy for trauma patients.

RECENT FINDINGS

Severe trauma is associated with a failure of multiple biologic emergency response systems that includes imbalanced inflammatory response, acute coagulopathy of trauma, and endovascular glycocalyx degradation with microcirculatory compromise. These abnormalities are all interlinked and related. Recent observations show that after severe trauma: proinflammatory and anti-inflammatory responses are concomitant, not sequential and resolution of the inflammatory response is an active process, not a passive one. Understanding these interrelated processes is considered extremely important for the development of future therapies for severe trauma in humans.

SUMMARY

Traumatic injuries continue to be a significant cause of mortality worldwide. Recent advances in understanding the mechanisms of end-organ failure, and modulation of the inflammatory response has important clinical implications regarding fluid resuscitation and treatment of coagulopathy.

Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle to myocardial infarction (MI). Significant cellular- and molecular-level contributors can be identified by analyzing data acquired through high-throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large-scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post-MI. In this review, we summarize the systems biology approaches needed to computationally simulate post-MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration.

The inverse problem in mathematical biology

Biological systems present particular challengers to model for the purposes of formulating predictions of generating biological insight. These systems are typically multi-scale, complex, and empirical observations are often sparse and subject to variability and uncertainty. This manuscript will review some of these specific challenges and introduce current methods used by modelers to construct meaningful solutions, in the context of preserving biological relevance. Opportunities to expand these methods are also discussed.