Andrew Abboud

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

A mathematical model of the human response to trauma replicates individual patient outcomes but predicts unexpected results in populations. A virtual large sample size Severe trauma or bleeding evokes an all-hands-on-deck immune response. When properly orchestrated, the myriad cytokines and peptides help to heal the patient. However, this process can easily go awry, and administering the right drug to compensate is a challenge. Brown and colleagues mathematically modeled the complicated immune responses from the data of 33 blunt trauma patients and then generated a larger cohort of 10,000 virtual trauma patients. This large virtual cohort predicted the reactions of smaller validation cohorts, but the surprise was that understanding the response details in a single patient did not predict how the population would act. The author’s virtual clinical trial indicated that inhibition of interleukin-6 (IL-6) produced a small survival benefit, whereas IL-1β inhibition did not help much and tumor necrosis factor–α made things worse. 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.

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.

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.

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.

An Enrichment Strategy Yields Seven Novel Single Nucleotide Polymorphisms Associated With Mortality and Altered Th17 Responses Following Blunt Trauma.

ABSTRACT Trauma is the leading cause of death worldwide for individuals under the age of 55. Interpatient genomic differences, in the form of candidate single-nucleotide polymorphisms (SNPs), have been associated previously with adverse outcomes after trauma. However, the utility of these SNPs to predict outcomes based on a meaningful endpoint such as survival is as yet undefined. We hypothesized that specific SNP haplotypes could segregate trauma survivors from non-survivors. Genomic DNA samples were obtained from 453 blunt trauma patients, for whom complete daily clinical and biomarker data were available for 397. Of these, 13 patients were non-survivors and the remaining 384 were survivors. All 397 DNA samples were amplified, fragmented, and examined for 551,839 SNPs using the Illumina Infinium CoreExome-24 v1.1 BeadChip (Illumina). To enrich for likely important SNPs, we initially compared SNPs of the 13 non-survivors versus 13 matched survivors, who were matched algorithmically for injury severity score (ISS), age, and gender ratio. This initial enrichment yielded 126 SNPs; a further comparison to the haplotypes of the remaining 371 survivors yielded a final total of 7 SNPs that distinguished survivors from non-survivors. Furthermore, severely injured survivors with the same seven SNPs as non-survivor exhibited distinct inflammatory responses from similarly injured survivors without those SNPs, and specifically had evidence of altered Th17 cell phenotypes based on computational modeling. These studies suggest an interaction among genetic polymorphism, injury severity, and initial inflammatory responses in driving trauma outcomes.

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.

IL-17A – A regulator in acute inflammation: Insights from in vitro, in vivo and in silico studies

Acute inflammation following sterile injury is both inevitable and necessary to restore homeostasis and promote tissue repair. However, when excessive, inflammation can jeopardize the viability of organs and cause detrimental systemic effects. Identifying key-regulators of the immune cascade induced by surgery is vital to attenuating excessive inflammation and its subsequent effects. In this review, we describe the emerging role of IL-17A as a key-regulator in acute inflammation. The role of IL-17A in chronic disease states, such as rheumatoid arthritis, psoriasis and cancer has been well documented, but its significance in acute inflammation following surgery, sepsis, or traumatic injury has not been well studied. We aim to highlight the role of IL-17A in acute inflammation caused by trauma, liver ischemia, and organ transplantation, as well as in post-operative surgical infections. Further investigation of the roles of this cytokine in acute inflammation may stimulate novel therapies or diagnostic modalities.

Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

Extracorporeal organ perfusion, in which organs are preserved in an isolated, ex vivo environment over an extended time-span, is a concept that has led to the development of numerous alternative preservation protocols designed to better maintain organ viability prior to transplantation. These protocols offer researchers a novel opportunity to obtain extensive sampling of isolated organs, free from systemic influences. Data-driven computational modeling is a primary means of integrating the extensive and multivariate data obtained in this fashion. In this review, we focus on the application of dynamic data-driven computational modeling to liver pathophysiology and transplantation based on data obtained from ex vivo organ perfusion.