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

Brown, David F.M.; Namas, Rami A.; Almahmoud, Khalid; Zaaqoq, Akram; Sarkar, Joydeep; Barclay, Derek; Yin, Jinling; Ghuma, Ali; Abboud, Andrew; Constantine, Gregory; Nieman, Gary F.; Zamora, Rubén; Chang, Steven C.; Billiar, Timothy R.; Vodovotz, Yoram

doi:10.1126/scitranslmed.aaa3636

Cited by 73 publications

(60 citation statements)

References 79 publications

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“…Over a decade of studies in cells, mice, large animals, and humans, we have developed an integrated view of the postinjury acute inflammatory response, which is regulated by initial chemokine-driven cues and propagated by DAMPs. These studies involved multiple paradigms of acute inflammation, including endotoxemia and experimental sepsis in mice (32,35,109,125), rats (39,107,112), and swine (43,50,112,116)), experimental trauma/ hemorrhagic shock in mice (35,45,81,84,97,125), and traumatic injury in humans (3,31,84,110,136,158).…”

Section: The Conundrum Of Acute Inflammationmentioning

confidence: 99%

“…As an adjunct to translational research in the setting of trauma, sepsis, and related inflammatory processes, we have suggested the concept of Translational Systems Biology (7,8,11,145,150,151). This concept encompasses the use of computational simulations of clinical trials (4,36,79,147), computational models of individuals to drive personalized medicine (31,84,116,136), streamlined usage of experimental murine animal models (146), and rational device design (106). The cornerstone of Translational Systems Biology is computational modeling (1,7,9,98,143,149,151), which could allow for overcoming translational challenges inherent to complex diseases.…”

Section: Translational Systems Biology Of Inflammationmentioning

confidence: 99%

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

Namas

et al. 2015

Antioxidants & Redox Signaling

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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 selfmaintaining 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. Antioxid. Redox Signal. 23, 1370-1387.Trauma: A Significant Burden T rauma/hemorrhagic shock remains the leading cause of death in patients younger than 45 years (70). It is the third leading cause of death worldwide, resulting in five million or 10% of all deaths annually and thus considered the fifth leading cause of significant disability (137). Traumatic injury is a pandemic disease, one that affects every nation in the world regardless of the level of socioeconomic development (70, 71).The disease is acute in onset, but often results in chronic, debilitating health problems that have effects beyond the individual victims. The financial impact of traumatic injuries is staggering: in 2000 in the United States, 10% of hospital discharges were due to injuries, and the direct cost of treating 50 million injury cases was $80.2 billion, with an estimated

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Section: The Conundrum Of Acute Inflammationmentioning

confidence: 99%

Section: Translational Systems Biology Of Inflammationmentioning

confidence: 99%

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

Namas

et al. 2015

Antioxidants & Redox Signaling

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“…Virtual populations have been used by others, particularly in the field of quantitative systems pharmacology (16,(22)(23)(24)(25)(26)(27)(28)(29)(30). Overwhelmingly (see, for instance, refs.…”

mentioning

confidence: 99%

“…These virtual population methods require dozens to thousands of samples to create virtual populations that are representative of the target population (23,26,(28)(29)(30). On the other hand, the VEPART procedure proposed herein works with very small amounts of data, and "expands" the available data in an effort to model the heterogeneity expected in the full population.…”

mentioning

confidence: 99%

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

Barish

Ochs

Sontag

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.robust therapies | cancer treatment | mathematical modeling | virotherapy | immunotherapy H eterogeneity is a defining feature of cancer (1, 2). Interpatient heterogeneity manifests clinically in variable disease progression and treatment response between patients with the same diagnosis, whereas intrapatient heterogeneity describes variations that exist between tumor cells in a single patient. Intrapatient heterogeneity can be broken down further into intratumor heterogeneity, intrametastatic heterogeneity, intermetastatic heterogeneity, and temporal heterogeneity (2, 3). Intratumor heterogeneity is evident through the presence of multiple genetic subclones within a primary tumor (2), which have even been shown to exist in spatially distinct regions of the primary tumor (4). Intrametastatic heterogeneity is similar to intratumor heterogeneity, but describes heterogeneity within a single metastatic lesion instead of within the primary tumor (2). Intermetastatic heterogeneity, on the other hand, describes variations in subclones between different metastases in the same patient (2). Finally, temporal heterogeneity is defined as changes that take place in the tumor over time, whether they are a result of genomic instability, natural selection, non-Darwinian evolution, or selective pressures imposed by treatment (4-6). Note that, in each case, heterogeneity need not be genetic but may also be epigenetic, phenotypic, or microenvironmental (2, 7, 8).For decades, cancer patients have been treated using standard of care, meaning they receive the best known treatment that has been deemed as efficacious and safe in epidemiological studies (9). However, in the face of such int...

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“…We utilize an HPC implementation of a previously developed system/population-level validated 37 ABM of the innate immune response [6] (the Innate Immune Response ABM or IIRABM) to generate a 38 first approximation behavioral landscape of sepsis to guide the development of suitable metrics with 39 which to characterize that behavioral landscape. Though developed over 15 years ago, the IIRABM 40 remains valid insomuch it reproduces the overall dynamics of the acute inflammatory response, 41 producing recognizable system level outcomes (healing, pro-inflammatory death, hypo-immune death and 42 overwhelming initial infection) using a knowledge-based rule system that, while admittedly incomplete, 43 has not been demonstrated to be incorrect in its represented behaviors by accumulated discoveries since 44 its development. In fact, some of the behaviors generated by the IIRABM presaged their general 45 recognition within the sepsis community, specifically the temporal concurrence of pro-and 46 anti-inflammatory cytokine responses (as opposed to sequential pro-and compensatory responses) [7,8] 47 and the importance of the immuno-incompetent/attenuated recovery phase of sepsis, particularly with characterization of the behavioral landscape of the IIRABM represents a lower bound, first 53 approximation of the ability to characterize the real world system.…”

mentioning

confidence: 99%

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

Cockrell

2017

Preprint

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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 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 for 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 near...

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

Cited by 73 publications

References 79 publications

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

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

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

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

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