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Mingo Barba S, Ademaj A, Marder D, Riesterer O, Lattuada M, Füchslin RM, Petri-Fink A, Scheidegger S. Theoretical evaluation of the impact of diverse treatment conditions by calculation of the tumor control probability (TCP) of simulated cervical cancer Hyperthermia-Radiotherapy (HT-RT) treatments in-silico. Int J Hyperthermia 2024; 41:2320852. [PMID: 38465653 DOI: 10.1080/02656736.2024.2320852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/15/2024] [Indexed: 03/12/2024] Open
Abstract
INTRODUCTION Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions. METHODS An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data. RESULTS Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect. CONCLUSION The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.
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Affiliation(s)
- Sergio Mingo Barba
- School of Engineering, Zürich University of Applied Sciences (ZHAW), Winterthur, Switzerland
- Chemistry Department, University of Fribourg, Fribourg, Switzerland
- Adolphe Merkle Institute, University of Fribourg, Fribourg, Switzerland
| | - Adela Ademaj
- Center for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
- Doctoral Clinical Science Program, Medical Faculty, University of Zurich, Zürich, Switzerland
| | - Dietmar Marder
- Center for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Oliver Riesterer
- Center for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Marco Lattuada
- Chemistry Department, University of Fribourg, Fribourg, Switzerland
| | - Rudolf M Füchslin
- School of Engineering, Zürich University of Applied Sciences (ZHAW), Winterthur, Switzerland
- European Centre for Living Technology, Venice, Italy
| | - Alke Petri-Fink
- Chemistry Department, University of Fribourg, Fribourg, Switzerland
- Adolphe Merkle Institute, University of Fribourg, Fribourg, Switzerland
| | - Stephan Scheidegger
- School of Engineering, Zürich University of Applied Sciences (ZHAW), Winterthur, Switzerland
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2
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Friedrich S, Friede T. On the role of benchmarking data sets and simulations in method comparison studies. Biom J 2024; 66:e2200212. [PMID: 36810737 DOI: 10.1002/bimj.202200212] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/26/2023] [Accepted: 02/01/2023] [Indexed: 02/24/2023]
Abstract
Method comparisons are essential to provide recommendations and guidance for applied researchers, who often have to choose from a plethora of available approaches. While many comparisons exist in the literature, these are often not neutral but favor a novel method. Apart from the choice of design and a proper reporting of the findings, there are different approaches concerning the underlying data for such method comparison studies. Most manuscripts on statistical methodology rely on simulation studies and provide a single real-world data set as an example to motivate and illustrate the methodology investigated. In the context of supervised learning, in contrast, methods are often evaluated using so-called benchmarking data sets, that is, real-world data that serve as gold standard in the community. Simulation studies, on the other hand, are much less common in this context. The aim of this paper is to investigate differences and similarities between these approaches, to discuss their advantages and disadvantages, and ultimately to develop new approaches to the evaluation of methods picking the best of both worlds. To this aim, we borrow ideas from different contexts such as mixed methods research and Clinical Scenario Evaluation.
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Affiliation(s)
- Sarah Friedrich
- Institute of Mathematics, University of Augsburg, Augsburg, Germany
- Centre for Advanced Analytics and Predictive Sciences, University of Augsburg, Augsburg, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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3
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Vodovotz Y. Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins. Trends Immunol 2023; 44:345-355. [PMID: 36967340 PMCID: PMC10147586 DOI: 10.1016/j.it.2023.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 04/05/2023]
Abstract
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.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA 15219, USA.
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4
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Schuurman AR, Sloot PMA, Wiersinga WJ, van der Poll T. Embracing complexity in sepsis. Crit Care 2023; 27:102. [PMID: 36906606 PMCID: PMC10007743 DOI: 10.1186/s13054-023-04374-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/19/2023] [Indexed: 03/13/2023] Open
Abstract
Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.
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Affiliation(s)
- Alex R Schuurman
- Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Peter M A Sloot
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - W Joost Wiersinga
- Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.,Division of Infectious Diseases, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands
| | - Tom van der Poll
- Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. .,Division of Infectious Diseases, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.
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5
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Cano-Gamez E, Burnham KL, Goh C, Allcock A, Malick ZH, Overend L, Kwok A, Smith DA, Peters-Sengers H, Antcliffe D. An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression. Sci Transl Med 2022; 14:eabq4433. [PMID: 36322631 PMCID: PMC7613832 DOI: 10.1126/scitranslmed.abq4433] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.
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Affiliation(s)
- Eddie Cano-Gamez
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK,Wellcome Sanger Institute, Wellcome Genome Campus; Cambridge, CB10 1SA, UK
| | - Katie L Burnham
- Wellcome Sanger Institute, Wellcome Genome Campus; Cambridge, CB10 1SA, UK
| | - Cyndi Goh
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK,The Jenner Institute, University of Oxford; Oxford, OX3 7DQ, UK
| | - Alice Allcock
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - Zunaira H. Malick
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - Lauren Overend
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - Andrew Kwok
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK
| | - David A. Smith
- Wellcome Centre for Human Genetics, University of Oxford; Oxford, OX3 7BN, UK,Chinese Academy of Medical Science Oxford Institute, University of Oxford; Oxford, OX3 7BN, UK
| | - Hessel Peters-Sengers
- Centre for Experimental and Molecular Medicine, Amsterdam University Medical Centers, University of Amsterdam; 1100 DD Amsterdam Southeast, Netherlands,Department of Epidemiology and Data Science, Amsterdam Public Health, Amsterdam University Medical Centers, University of Amsterdam, 1100 DD Amsterdam Southeast, Netherlands,The Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centers, 1100 DD Amsterdam Southeast, Netherlands
| | - David Antcliffe
- Division of Anaesthesia, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London; London, SW7 2AZ, UK
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6
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Baratchart E, Lo CH, Lynch CC, Basanta D. Integrated computational and in vivo models reveal Key Insights into macrophage behavior during bone healing. PLoS Comput Biol 2022; 18:e1009839. [PMID: 35559958 PMCID: PMC9106165 DOI: 10.1371/journal.pcbi.1009839] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 01/17/2022] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- Etienne Baratchart
- Integrated Mathematical Oncology Department, SRB4, Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
| | - Chen Hao Lo
- Cancer Biology Ph.D. Program, Department of Cell Biology Microbiology and Molecular Biology, University of South Florida, Tampa, Florida, United States of America
- Tumor Biology Department, SRB3, Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
| | - Conor C. Lynch
- Cancer Biology Ph.D. Program, Department of Cell Biology Microbiology and Molecular Biology, University of South Florida, Tampa, Florida, United States of America
- * E-mail: (CL); (DB)
| | - David Basanta
- Integrated Mathematical Oncology Department, SRB4, Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
- * E-mail: (CL); (DB)
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7
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Nanayakkara T, Clermont G, Langmead CJ, Swigon D. Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. PLOS DIGITAL HEALTH 2022; 1:e0000012. [PMID: 36812511 PMCID: PMC9931225 DOI: 10.1371/journal.pdig.0000012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022]
Abstract
Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies, that are consistent with clinical knowledge. Further our method consistently identifies high-risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research.
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Affiliation(s)
- Thesath Nanayakkara
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, 15213, United States of America,* E-mail:
| | - Gilles Clermont
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, University of Pittsburgh School of Medicine Pittsburgh, PA, 15213, United States of America
| | - Christopher James Langmead
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, United States of America
| | - David Swigon
- Department of Mathematics, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, United States of America
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8
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An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. NATURE CANCER 2022; 2:709-722. [PMID: 35121948 DOI: 10.1038/s43018-021-00236-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/14/2021] [Indexed: 12/11/2022]
Abstract
Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual's disease course unfolds.
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9
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Ramirez-Zuniga I, Rubin JE, Swigon D, Redl H, Clermont G. A data-driven model of the role of energy in sepsis. J Theor Biol 2022; 533:110948. [PMID: 34757193 DOI: 10.1016/j.jtbi.2021.110948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 09/05/2021] [Accepted: 10/24/2021] [Indexed: 01/13/2023]
Abstract
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.
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Affiliation(s)
- Ivan Ramirez-Zuniga
- University of Pittsburgh, Department of Mathematics, Pittsburgh, PA, United States
| | - Jonathan E Rubin
- University of Pittsburgh, Department of Mathematics, Pittsburgh, PA, United States
| | - David Swigon
- University of Pittsburgh, Department of Mathematics, Pittsburgh, PA, United States; McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, Pittsburgh, United States
| | - Heinz Redl
- Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, AUVA Trauma Research Center, Vienna, Austria; Technical University Vienna, Vienna, Austria
| | - Gilles Clermont
- University of Pittsburgh, Department of Mathematics, Pittsburgh, PA, United States; University of Pittsburgh, Department of Critical Care Medicine, Pittsburgh, PA, United States
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10
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Day JD, Park S, Ranard BL, Singh H, Chow CC, Vodovotz Y. Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model. Front Immunol 2021; 12:754127. [PMID: 34777366 PMCID: PMC8582279 DOI: 10.3389/fimmu.2021.754127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/04/2021] [Indexed: 01/08/2023] Open
Abstract
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.
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Affiliation(s)
- Judy D Day
- Department of Mathematics, University of Tennessee, Knoxville, TN, United States.,Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville, TN, United States
| | - Soojin Park
- Department of Neurology & Division of Critical Care and Hospital Neurology, Columbia University College of Physicians and Surgeons, New York Presbyterian Hospital - Columbia University Irving Medical Center, New York, NY, United States.,Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Benjamin L Ranard
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, United States.,Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Columbia University College of Physicians and Surgeons, New York Presbyterian Hospital - Columbia University Irving Medical Center, New York, NY, United States
| | - Harinder Singh
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, United States.,Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Carson C Chow
- Mathematical Biology Section, Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, United States
| | - Yoram Vodovotz
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States.,Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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11
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Imami AS, McCullumsmith RE, O’Donovan SM. Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example. Transl Psychiatry 2021; 11:591. [PMID: 34785660 PMCID: PMC8594646 DOI: 10.1038/s41398-021-01724-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 02/07/2023] Open
Abstract
Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.
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Affiliation(s)
- Ali S. Imami
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA
| | - Robert E. McCullumsmith
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA ,grid.422550.40000 0001 2353 4951Neurosciences Institute, Promedica, Toledo, OH USA
| | - Sinead M. O’Donovan
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA
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12
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Gutiérrez-Casares JR, Quintero J, Jorba G, Junet V, Martínez V, Pozo-Rubio T, Oliva B, Daura X, Mas JM, Montoto C. Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate. Front Psychiatry 2021; 12:741170. [PMID: 34803764 PMCID: PMC8595241 DOI: 10.3389/fpsyt.2021.741170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Regulatory agencies encourage computer modeling and simulation to reduce the time and cost of clinical trials. Although still not classified in formal guidelines, system biology-based models represent a powerful tool for generating hypotheses with great molecular detail. Herein, we have applied a mechanistic head-to-head in silico clinical trial (ISCT) between two treatments for attention-deficit/hyperactivity disorder, to wit lisdexamfetamine (LDX) and methylphenidate (MPH). The ISCT was generated through three phases comprising (i) the molecular characterization of drugs and pathologies, (ii) the generation of adult and children virtual populations (vPOPs) totaling 2,600 individuals and the creation of physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, and (iii) data analysis with artificial intelligence methods. The characteristics of our vPOPs were in close agreement with real reference populations extracted from clinical trials, as did our PBPK models with in vivo parameters. The mechanisms of action of LDX and MPH were obtained from QSP models combining PBPK modeling of dosing schemes and systems biology-based modeling technology, i.e., therapeutic performance mapping system. The step-by-step process described here to undertake a head-to-head ISCT would allow obtaining mechanistic conclusions that could be extrapolated or used for predictions to a certain extent at the clinical level. Altogether, these computational techniques are proven an excellent tool for hypothesis-generation and would help reach a personalized medicine.
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Affiliation(s)
- José Ramón Gutiérrez-Casares
- Unidad Ambulatoria de Psiquiatría y Salud Mental de la Infancia, Niñez y Adolescencia, Hospital Perpetuo Socorro, Badajoz, Spain
| | - Javier Quintero
- Servicio de Psiquiatría, Hospital Universitario Infanta Leonor, Universidad Complutense, Madrid, Spain
| | - Guillem Jorba
- Anaxomics Biotech, Barcelona, Spain
- Research Programme on Biomedical Informatics (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
| | - Valentin Junet
- Anaxomics Biotech, Barcelona, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | | | | | - Baldomero Oliva
- Research Programme on Biomedical Informatics (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
| | - Xavier Daura
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | | | - Carmen Montoto
- Medical Department, Takeda Farmacéutica España, Madrid, Spain
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13
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Kreutzer FP, Meinecke A, Schmidt K, Fiedler J, Thum T. Alternative strategies in cardiac preclinical research and new clinical trial formats. Cardiovasc Res 2021; 118:746-762. [PMID: 33693475 PMCID: PMC7989574 DOI: 10.1093/cvr/cvab075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 03/03/2021] [Indexed: 02/07/2023] Open
Abstract
An efficient and safe drug development process is crucial for the establishment of new drugs on the market aiming to increase quality of life and life-span of our patients. Despite technological advances in the past decade, successful launches of drug candidates per year remain low. We here give an overview about some of these advances and suggest improvements for implementation to boost preclinical and clinical drug development with a focus on the cardiovascular field. We highlight advantages and disadvantages of animal experimentation and thoroughly review alternatives in the field of three-dimensional cell culture as well as preclinical use of spheroids and organoids. Microfluidic devices and their potential as organ-on-a-chip systems, as well as the use of living animal and human cardiac tissues are additionally introduced. In the second part, we examine recent gold standard randomized clinical trials and present possible modifications to increase lead candidate throughput: adaptive designs, master protocols, and drug repurposing. In silico and N-of-1 trials have the potential to redefine clinical drug candidate evaluation. Finally, we briefly discuss clinical trial designs during pandemic times.
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Affiliation(s)
- Fabian Philipp Kreutzer
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany
| | - Anna Meinecke
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany
| | - Kevin Schmidt
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany
| | - Jan Fiedler
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany.,REBIRTH Center for Translational Regenerative Medicine, Hannover Medical School, Hannover, Germany.,Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hannover, Germany
| | - Thomas Thum
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany.,REBIRTH Center for Translational Regenerative Medicine, Hannover Medical School, Hannover, Germany.,Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hannover, Germany
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14
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15
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Alfonso S, Jenner AL, Craig M. Translational approaches to treating dynamical diseases through in silico clinical trials. CHAOS (WOODBURY, N.Y.) 2020; 30:123128. [PMID: 33380031 DOI: 10.1063/5.0019556] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
The primary goal of drug developers is to establish efficient and effective therapeutic protocols. Multifactorial pathologies, including dynamical diseases and complex disorders, can be difficult to treat, given the high degree of inter- and intra-patient variability and nonlinear physiological relationships. Quantitative approaches combining mechanistic disease modeling and computational strategies are increasingly leveraged to rationalize pre-clinical and clinical studies and to establish effective treatment strategies. The development of clinical trials has led to new computational methods that allow for large clinical data sets to be combined with pharmacokinetic and pharmacodynamic models of diseases. Here, we discuss recent progress using in silico clinical trials to explore treatments for a variety of complex diseases, ultimately demonstrating the immense utility of quantitative methods in drug development and medicine.
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Affiliation(s)
- Sofia Alfonso
- Department of Physiology, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Morgan Craig
- Department of Physiology, McGill University, Montreal, Quebec H3A 0G4, Canada
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16
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Rogers KV, Martin SW, Bhattacharya I, Singh RSP, Nayak S. A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 1 - Model Framework. Clin Transl Sci 2020; 14:239-248. [PMID: 32822108 PMCID: PMC7877855 DOI: 10.1111/cts.12849] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 06/14/2020] [Indexed: 12/14/2022] Open
Abstract
A mechanistic, multistate, mathematical model of inflammatory bowel disease (IBD) was developed by including key biological mechanisms in blood and gut, including cell differentiation, cytokine production, and clinical biomarkers. The model structure is consistent between healthy volunteers and IBD disease phenotype, with 24 parameters changed between diseases. Modular nature of the model allows for easy incorporation of new mechanisms or modification of existing interactions. Model simulations for steady-state levels of proteins and cells in the blood and gut using a population approach are consistent with published data. By simulating the response of two clinical biomarkers, C-reactive protein and fecal calprotectin, to parameter perturbations, the model explores hypotheses for possible treatment mechanisms. With additional experimental validation and addition of drug treatments, the model provides a platform to test hypothesis on treatment effects in IBD.
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Affiliation(s)
- Katharine V Rogers
- Biologics Development Sciences, Janssen Biotherapeutics, Janssen Research & Development, LLC, Raritan, New Jersey, USA
| | - Steven W Martin
- Pharmacometrics, Global Clinical Pharmacology, Pfizer Inc., Cambridge, Massachusetts, USA
| | | | | | - Satyaprakash Nayak
- Pharmacometrics, Global Clinical Pharmacology, Pfizer Inc., Cambridge, Massachusetts, USA
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17
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Tallon J, Browning B, Couenne F, Bordes C, Venet F, Nony P, Gueyffier F, Moucadel V, Monneret G, Tayakout-Fayolle M. Dynamical modeling of pro- and anti-inflammatory cytokines in the early stage of septic shock. In Silico Biol 2020; 14:101-121. [PMID: 32597796 PMCID: PMC7505012 DOI: 10.3233/isb-200474] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
A dynamical model of the pathophysiological behaviors of IL18 and IL10 cytokines with their receptors is tested against data for the case of early sepsis. The proposed approach considers the surroundings (organs and bone marrow) and the different subsystems (cells and cyctokines). The interactions between blood cells, cytokines and the surroundings are described via mass balances. Cytokines are adsorbed onto associated receptors at the cell surface. The adsorption is described by the Langmuir model and gives rise to the production of more cytokines and associated receptors inside the cell. The quantities of pro and anti-inflammatory cytokines present in the body are combined to give global information via an inflammation level function which describes the patient’s state. Data for parameter estimation comes from the Sepsis 48 H database. Comparisons between patient data and simulations are presented and are in good agreement. For the IL18/IL10 cytokine pair, 5 key parameters have been found. They are linked to pro-inflammatory IL18 cytokine and show that the early sepsis is driven by components of inflammatory character.
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Affiliation(s)
- J Tallon
- Université Claude Bernard Lyon 1, CNRS, LAGEPP UMR 5007, Villeurbanne, France
| | - B Browning
- Université Claude Bernard Lyon 1, CNRS, LAGEPP UMR 5007, Villeurbanne, France
| | - F Couenne
- Université Claude Bernard Lyon 1, CNRS, LAGEPP UMR 5007, Villeurbanne, France
| | - C Bordes
- Université Claude Bernard Lyon 1, CNRS, LAGEPP UMR 5007, Villeurbanne, France
| | - F Venet
- Hospices Civils de Lyon, LYON Cedex 03 - France
| | - P Nony
- Université Claude Bernard Lyon 1, CNRS, LBBE UMR 5558, Lyon, France
| | - F Gueyffier
- Université Claude Bernard Lyon 1, CNRS, LBBE UMR 5558, Lyon, France
| | | | - G Monneret
- Hospices Civils de Lyon, LYON Cedex 03 - France
| | - M Tayakout-Fayolle
- Université Claude Bernard Lyon 1, CNRS, LAGEPP UMR 5007, Villeurbanne, France
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18
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Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, dos Santos RW, Wang L. Creation and application of virtual patient cohorts of heart models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190558. [PMID: 32448064 PMCID: PMC7287335 DOI: 10.1098/rsta.2019.0558] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2020] [Indexed: 05/21/2023]
Abstract
Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
| | | | | | | | | | - E. M. Cherry
- Georgia Institute of Technology, Atlanta, GA, USA
| | - T. Delhaas
- Maastricht University, Maastricht, the Netherlands
| | - F. H. Fenton
- Georgia Institute of Technology, Atlanta, GA, USA
| | - A. V. Panfilov
- Ghent University, Gent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia
| | - P. Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Administration, Rockville, MD, USA
| | - G. Plank
- Medical University of Graz, Graz, Austria
| | | | | | | | - L. Wang
- Rochester Institute of Technology, La JollaRochester, NY, USA
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19
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Lilley E, Andrews MR, Bradbury EJ, Elliott H, Hawkins P, Ichiyama RM, Keeley J, Michael-Titus AT, Moon LDF, Pluchino S, Riddell J, Ryder K, Yip PK. Refining rodent models of spinal cord injury. Exp Neurol 2020; 328:113273. [PMID: 32142803 DOI: 10.1016/j.expneurol.2020.113273] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/28/2020] [Accepted: 03/02/2020] [Indexed: 01/16/2023]
Abstract
This report was produced by an Expert Working Group (EWG) consisting of UK-based researchers, veterinarians and regulators of animal experiments with specialist knowledge of the use of animal models of spinal cord injury (SCI). It aims to facilitate the implementation of the Three Rs (Replacement, Reduction and Refinement), with an emphasis on refinement. Specific animal welfare issues were identified and discussed, and practical measures proposed, with the aim of reducing animal use and suffering, reducing experimental variability, and increasing translatability within this critically important research field.
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Affiliation(s)
- Elliot Lilley
- Research Animals Department, Royal Society for the Prevention of Cruelty to Animals, Wilberforce Way, Southwater, Horsham, West Sussex RH13 9RS, UK.
| | - Melissa R Andrews
- Biological Sciences, University of Southampton, 3059, Life Sciences Bldg 85, Highfield Campus, Southampton SO17 1BJ, UK.
| | - Elizabeth J Bradbury
- King's College London, Regeneration Group, Wolfson Centre for Age-Related Diseases, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), Guy's Campus, London SE1 1UL, UK.
| | - Heather Elliott
- Animals in Scientific Research Unit, 14th Floor, Lunar House, 40 Wellesley Road, Croydon CR9 2BY, UK.
| | - Penny Hawkins
- Research Animals Department, Royal Society for the Prevention of Cruelty to Animals, Wilberforce Way, Southwater, Horsham, West Sussex RH13 9RS, UK.
| | - Ronaldo M Ichiyama
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, UK.
| | - Jo Keeley
- University Biomedical Services, University of Cambridge, Greenwich House, Madingley Rise, Madingley Road, Cambridge CB3 0TX, UK.
| | - Adina T Michael-Titus
- Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark St, London E1 2AT, UK.
| | - Lawrence D F Moon
- King's College London, Regeneration Group, Wolfson Centre for Age-Related Diseases, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), Guy's Campus, London SE1 1UL, UK.
| | - Stefano Pluchino
- University Biomedical Services, University of Cambridge, Greenwich House, Madingley Rise, Madingley Road, Cambridge CB3 0TX, UK.
| | - John Riddell
- Spinal Cord Group, Institute of Neuroscience and Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK.
| | - Kathy Ryder
- Animals in Scientific Research Unit, 14th Floor, Lunar House, 40 Wellesley Road, Croydon CR9 2BY, UK.
| | - Ping K Yip
- Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark St, London E1 2AT, UK.
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20
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Abudukelimu A, Barberis M, Redegeld F, Sahin N, Sharma RP, Westerhoff HV. Complex Stability and an Irrevertible Transition Reverted by Peptide and Fibroblasts in a Dynamic Model of Innate Immunity. Front Immunol 2020; 10:3091. [PMID: 32117197 PMCID: PMC7033641 DOI: 10.3389/fimmu.2019.03091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 12/17/2019] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Abulikemu Abudukelimu
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.,Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.,Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.,Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom
| | - Frank Redegeld
- Division of Pharmacology, Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Nilgun Sahin
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands
| | - Raju P Sharma
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands
| | - Hans V Westerhoff
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.,Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands.,School for Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom.,Systems Biology Amsterdam, VU University Amsterdam, Amsterdam, Netherlands
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21
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Sun CL, Karlsson L, Torp-Pedersen C, Morrison LJ, Brooks SC, Folke F, Chan TC. In Silico Trial of Optimized Versus Actual Public Defibrillator Locations. J Am Coll Cardiol 2019; 74:1557-1567. [DOI: 10.1016/j.jacc.2019.06.075] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 06/06/2019] [Accepted: 06/16/2019] [Indexed: 11/30/2022]
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22
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Garg A, Yuen S, Seekhao N, Yu G, Karwowski JAC, Powell M, Sakata JT, Mongeau L, JaJa J, Li-Jessen NYK. Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification. APPLIED SCIENCES (BASEL, SWITZERLAND) 2019; 9:2974. [PMID: 31372307 PMCID: PMC6675024 DOI: 10.3390/app9152974] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.
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Affiliation(s)
- Aman Garg
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Samson Yuen
- School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 1G1, Canada
| | - Nuttiiya Seekhao
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Grace Yu
- School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 1G1, Canada
| | | | - Michael Powell
- Virginia Tech Carilion Research Institute, Roanoke, VA 24016, USA
| | - Jon T. Sakata
- Department of Biology, McGill University, Montreal, QC H3A 1G1, Canada
| | - Luc Mongeau
- Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Joseph JaJa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Nicole Y. K. Li-Jessen
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
- School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 1G1, Canada
- Department of Otolaryngology–Head and Neck Surgery, McGill University, Montreal, QC H3A 1G1, Canada
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23
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Lamparello AJ, Namas RA, Constantine G, McKinley TO, Elster E, Vodovotz Y, Billiar TR. A conceptual time window-based model for the early stratification of trauma patients. J Intern Med 2019; 286:2-15. [PMID: 30623510 DOI: 10.1111/joim.12874] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Progress in the testing of therapies targeting the immune response following trauma, a leading cause of morbidity and mortality worldwide, has been slow. We propose that the design of interventional trials in trauma would benefit from a scheme or platform that could support the identification and implementation of prognostic strategies for patient stratification. Here, we propose a stratification scheme based on defined time periods or windows following the traumatic event. This 'time-window' model allows for the incorporation of prognostic variables ranging from circulating biomarkers and clinical data to patient-specific information such as gene variants to predict adverse short- or long-term outcomes. A number of circulating biomarkers, including cell injury markers and damage-associated molecular patterns (DAMPs), and inflammatory mediators have been shown to correlate with adverse outcomes after trauma. Likewise, several single nucleotide polymorphisms (SNPs) associate with complications or death in trauma patients. This review summarizes the status of our understanding of the prognostic value of these classes of variables in predicting outcomes in trauma patients. Strategies for the incorporation of these prognostic variables into schemes designed to stratify trauma patients, such as our time-window model, are also discussed.
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Affiliation(s)
- A J Lamparello
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - R A Namas
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - G Constantine
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - T O McKinley
- Department of Orthopaedic Surgery, Indiana University School of Medicine, IU Health Methodist Hospital, Indianapolis, IN, USA
| | - E Elster
- Department of Surgery, University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Y Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - T R Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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24
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Day JD, Cockrell C, Namas R, Zamora R, An G, Vodovotz Y. Inflammation and Disease: Modelling and Modulation of the Inflammatory Response to Alleviate Critical Illness. CURRENT OPINION IN SYSTEMS BIOLOGY 2018; 12:22-29. [PMID: 30886940 PMCID: PMC6420220 DOI: 10.1016/j.coisb.2018.08.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Judy D. Day
- Departments of Mathematics and Electrical Engineering & Computer Science, University of Tennessee, USA
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, USA
| | | | - Rami Namas
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, USA
- Department of Surgery, University of Pittsburgh, USA
| | - Ruben Zamora
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, USA
- Department of Surgery, University of Pittsburgh, USA
| | - Gary An
- Department of Surgery, University of Chicago, USA
| | - Yoram Vodovotz
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, USA
- Department of Surgery, University of Pittsburgh, USA
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25
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Talisa VB, Yende S, Seymour CW, Angus DC. Arguing for Adaptive Clinical Trials in Sepsis. Front Immunol 2018; 9:1502. [PMID: 30002660 PMCID: PMC6031704 DOI: 10.3389/fimmu.2018.01502] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 06/18/2018] [Indexed: 12/13/2022] Open
Abstract
Sepsis is life-threatening organ dysfunction due to dysregulated response to infection. Patients with sepsis exhibit wide heterogeneity stemming from genetic, molecular, and clinical factors as well as differences in pathogens, creating challenges for the development of effective treatments. Several gaps in knowledge also contribute: (i) biomarkers that identify patients likely to benefit from specific treatments are unknown; (ii) therapeutic dose and duration is often poorly understood; and (iii) short-term mortality, a common outcome measure, is frequently criticized for being insensitive. To date, the majority of sepsis trials use traditional design features, and have largely failed to identify new treatments with incremental benefit over standard of care. Traditional trials are also frequently conducted as part of a drug evaluation process that is segmented into several phases, each requiring separate trials, with a long time delay from inception through design and execution to incorporation of results into clinical practice. By contrast, adaptive clinical trial designs facilitate the evaluation of several candidate treatments simultaneously, learn from emergent discoveries during the course of the trial, and can be structured efficiently to lead to more timely conclusions compared to traditional trial designs. Adoption of new treatments in clinical practice can be accelerated if these trials are incorporated in electronic health records as part of a learning health system. In this review, we discuss challenges in the evaluation of treatments for sepsis, and explore potential benefits and weaknesses of recent advances in adaptive trial methodologies to address these challenges.
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Affiliation(s)
| | - Sachin Yende
- Clinical Research, Investigation, and Systems Modeling of Acute Illness Center, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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26
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A Mathematical Model of the Inflammatory Response to Pathogen Challenge. Bull Math Biol 2018; 80:2242-2271. [DOI: 10.1007/s11538-018-0459-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 06/18/2018] [Indexed: 12/18/2022]
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27
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Lazaridis C, Rusin CG, Robertson CS. Secondary brain injury: Predicting and preventing insults. Neuropharmacology 2018; 145:145-152. [PMID: 29885419 DOI: 10.1016/j.neuropharm.2018.06.005] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/07/2018] [Accepted: 06/04/2018] [Indexed: 11/17/2022]
Abstract
Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. We, and others, have recently developed predictive models based on machine learning from continuous time series of intracranial pressure and partial brain tissue oxygenation. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In this article, we review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithms. This article is part of the Special Issue entitled "Novel Treatments for Traumatic Brain Injury".
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Affiliation(s)
- Christos Lazaridis
- Division of Neurocritical Care, Department of Neurology, Baylor College of Medicine, Houston, TX, United States; Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States.
| | - Craig G Rusin
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Claudia S Robertson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States.
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Seekhao N, Shung C, JaJa J, Mongeau L, Li-Jessen NYK. High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair. Front Physiol 2018; 9:304. [PMID: 29706894 PMCID: PMC5906585 DOI: 10.3389/fphys.2018.00304] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 03/13/2018] [Indexed: 01/13/2023] Open
Abstract
Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.
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Affiliation(s)
- Nuttiiya Seekhao
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Caroline Shung
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Joseph JaJa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Luc Mongeau
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Nicole Y K Li-Jessen
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
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Abstract
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.
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Carlier A, Vasilevich A, Marechal M, de Boer J, Geris L. In silico clinical trials for pediatric orphan diseases. Sci Rep 2018; 8:2465. [PMID: 29410461 PMCID: PMC5802824 DOI: 10.1038/s41598-018-20737-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 01/15/2018] [Indexed: 12/14/2022] Open
Abstract
To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Each virtual subject was simulated to receive no treatment and bone morphogenetic protein (BMP) treatment. We have shown that the degree of severity of CPT is significantly reduced with BMP treatment, although the effect is highly subject-specific. Using machine learning techniques we were also able to stratify the virtual subject population in adverse responders, non-responders, responders and asymptomatic. In summary, this study shows the potential of in silico medicine technologies as well as their implications for other orphan diseases.
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Affiliation(s)
- A Carlier
- Biomechanics Section, KU Leuven, Celestijnenlaan 300C, PB 2419, 3000 Leuven, Belgium and Biomechanics Research Unit, University of Liège, Chemin des Chevreuils 1 - BAT 52/3, 4000, Liège 1, Belgium.,Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, O&N 1, Herestraat 49, PB 813, 3000, Leuven, Belgium.,MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - A Vasilevich
- MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - M Marechal
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, O&N 1, Herestraat 49, PB 813, 3000, Leuven, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, O&N 1, Herestraat 49, PB 813, 3000, Leuven, Belgium
| | - J de Boer
- MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - L Geris
- Biomechanics Section, KU Leuven, Celestijnenlaan 300C, PB 2419, 3000 Leuven, Belgium and Biomechanics Research Unit, University of Liège, Chemin des Chevreuils 1 - BAT 52/3, 4000, Liège 1, Belgium. .,Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, O&N 1, Herestraat 49, PB 813, 3000, Leuven, Belgium.
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31
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Fuertinger DH, Topping A, Kappel F, Thijssen S, Kotanko P. The Virtual Anemia Trial: An Assessment of Model-Based In Silico Clinical Trials of Anemia Treatment Algorithms in Patients With Hemodialysis. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:219-227. [PMID: 29368434 PMCID: PMC5915606 DOI: 10.1002/psp4.12276] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/29/2017] [Accepted: 12/28/2017] [Indexed: 12/13/2022]
Abstract
In silico approaches have been proposed as a novel strategy to increase the repertoire of clinical trial designs. Realistic simulations of clinical trials can provide valuable information regarding safety and limitations of treatment protocols and have been shown to assist in the cost‐effective planning of clinical studies. In this report, we present a blueprint for the stepwise integration of internal, external, and ecological validity considerations in virtual clinical trials (VCTs). We exemplify this approach in the context of a model‐based in silico clinical trial aimed at anemia treatment in patients undergoing hemodialysis (HD). Hemoglobin levels and subsequent anemia treatment were simulated on a per patient level over the course of a year and compared to real‐life clinical data of 79,426 patients undergoing HD. The novel strategies presented here, aimed to improve external and ecological validity of a VCT, significantly increased the predictive power of the discussed in silico trial.
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Affiliation(s)
- Doris H Fuertinger
- Renal Research Institute, New York, New York, USA.,Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany
| | | | - Franz Kappel
- Institute for Mathematics and Scientific Computing, Karl-Franzens University, Graz, Austria
| | | | - Peter Kotanko
- Renal Research Institute, New York, New York, USA.,Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Herzog SA, Blaizot S, Hens N. Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review. BMC Infect Dis 2017; 17:775. [PMID: 29254504 PMCID: PMC5735541 DOI: 10.1186/s12879-017-2874-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 11/30/2017] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Mathematical models offer the possibility to investigate the infectious disease dynamics over time and may help in informing design of studies. A systematic review was performed in order to determine to what extent mathematical models have been incorporated into the process of planning studies and hence inform study design for infectious diseases transmitted between humans and/or animals. METHODS We searched Ovid Medline and two trial registry platforms (Cochrane, WHO) using search terms related to infection, mathematical model, and study design from the earliest dates to October 2016. Eligible publications and registered trials included mathematical models (compartmental, individual-based, or Markov) which were described and used to inform the design of infectious disease studies. We extracted information about the investigated infection, population, model characteristics, and study design. RESULTS We identified 28 unique publications but no registered trials. Focusing on compartmental and individual-based models we found 12 observational/surveillance studies and 11 clinical trials. Infections studied were equally animal and human infectious diseases for the observational/surveillance studies, while all but one between humans for clinical trials. The mathematical models were used to inform, amongst other things, the required sample size (n = 16), the statistical power (n = 9), the frequency at which samples should be taken (n = 6), and from whom (n = 6). CONCLUSIONS Despite the fact that mathematical models have been advocated to be used at the planning stage of studies or surveillance systems, they are used scarcely. With only one exception, the publications described theoretical studies, hence, not being utilised in real studies.
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Affiliation(s)
- Sereina A. Herzog
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Stéphanie Blaizot
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Niel Hens
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium
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33
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Christ B, Dahmen U, Herrmann KH, König M, Reichenbach JR, Ricken T, Schleicher J, Ole Schwen L, Vlaic S, Waschinsky N. Computational Modeling in Liver Surgery. Front Physiol 2017; 8:906. [PMID: 29249974 PMCID: PMC5715340 DOI: 10.3389/fphys.2017.00906] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 10/25/2017] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Bruno Christ
- Molecular Hepatology Lab, Clinics of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Matthias König
- Department of Biology, Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Tim Ricken
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
| | - Jana Schleicher
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany.,Department of Bioinformatics, Friedrich Schiller University Jena, Jena, Germany
| | | | - Sebastian Vlaic
- Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
| | - Navina Waschinsky
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
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35
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Abstract
Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.
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36
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Namas R, Ghuma A, Hermus L, Zamora R, Okonkwo D, Billiar T, Vodovotz Y. The Acute Inflammatory Response in Trauma /Hemorrhage and Traumatic Brain Injury: Current State and Emerging Prospects. Libyan J Med 2016. [DOI: 10.3402/ljm.v4i3.4824] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
| | | | - L. Hermus
- Martini Hospital, Department of Surgery, Groningen, Netherlands
| | | | | | | | - Y. Vodovotz
- Department of Surgery
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine University of Pittsburgh, Pittsburgh, PA
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37
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Kim E, Rebecca VW, Smalley KSM, Anderson ARA. Phase i trials in melanoma: A framework to translate preclinical findings to the clinic. Eur J Cancer 2016; 67:213-222. [PMID: 27689717 PMCID: PMC5658204 DOI: 10.1016/j.ejca.2016.07.024] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 07/20/2016] [Accepted: 07/25/2016] [Indexed: 01/30/2023]
Abstract
BACKGROUND One of major issues in clinical trials in oncology is their high failure rate, despite the fact that the trials were designed based on the data from successful equivalent preclinical studies. This is in part due to the intrinsic homogeneity of preclinical model systems and the contrasting heterogeneity of actual patient responses. METHODS We present a mathematical model-driven framework, phase i (virtual/imaginary) trials, that integrates the heterogeneity of actual patient responses and preclinical studies through a cohort of virtual patients. The framework includes an experimentally calibrated mathematical model, a cohort of heterogeneous virtual patients, an assessment of stratification factors, and treatment optimisation. We show the detailed process through the lens of melanoma combination therapy (chemotherapy and an AKT inhibitor), using both preclinical and clinical data. RESULTS The mathematical model predicts melanoma treatment response and resistance to mono and combination therapies and was calibrated and then validated with in vitro experimental data. The validated model and a genetic algorithm were used to generate virtual patients whose tumour volume responses to the combination therapy matched statistically the actual heterogeneous patient responses in the clinical trial. Analyses on simulated cohorts revealed key model parameters such as a tumour volume doubling rate and a therapy-induced phenotypic switch rate that may have clinical correlates. Finally, our approach predicts optimal AKT inhibitor scheduling suggesting more effective but less toxic treatment strategies. CONCLUSION Our proposed computational framework to implement phase i trials in cancer can readily capture observed heterogeneous clinical outcomes and predict patient survival. Importantly, phase i trials can be used to optimise future clinical trial design.
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Affiliation(s)
- Eunjung Kim
- Integrated Mathematical Oncology Department, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
| | - Vito W Rebecca
- The Department of Tumor Biology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Keiran S M Smalley
- The Department of Tumor Biology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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38
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Clancy CE, An G, Cannon WR, Liu Y, May EE, Ortoleva P, Popel AS, Sluka JP, Su J, Vicini P, Zhou X, Eckmann DM. Multiscale Modeling in the Clinic: Drug Design and Development. Ann Biomed Eng 2016; 44:2591-610. [PMID: 26885640 PMCID: PMC4983472 DOI: 10.1007/s10439-016-1563-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 02/02/2016] [Indexed: 01/30/2023]
Abstract
A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.
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Affiliation(s)
- Colleen E Clancy
- Department of Pharmacology, University of California, Davis, CA, USA.
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, USA
| | - William R Cannon
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Elebeoba E May
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Peter Ortoleva
- Department of Chemistry, Indiana University, Bloomington, IN, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - James P Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Jing Su
- Department of Radiology, Wake Forest University, Winston-Salem, NC, USA
| | - Paolo Vicini
- Clinical Pharmacology and DMPK, MedImmune, Cambridge, UK
| | - Xiaobo Zhou
- Department of Radiology, Wake Forest University, Winston-Salem, NC, USA
| | - David M Eckmann
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
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Alam MA, Subramanyam Rallabandi VP, Roy PK. Systems Biology of Immunomodulation for Post-Stroke Neuroplasticity: Multimodal Implications of Pharmacotherapy and Neurorehabilitation. Front Neurol 2016; 7:94. [PMID: 27445961 PMCID: PMC4923163 DOI: 10.3389/fneur.2016.00094] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 06/07/2016] [Indexed: 12/13/2022] Open
Abstract
AIMS Recent studies indicate that anti-inflammatory drugs, act as a double-edged sword, not only exacerbating secondary brain injury but also contributing to neurological recovery after stroke. Our aim is to explore whether there is a beneficial role for neuroprotection and functional recovery using anti-inflammatory drug along with neurorehabilitation therapy using transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), so as to improve functional recovery after ischemic stroke. METHODS We develop a computational systems biology approach from preclinical data, using ordinary differential equations, to study the behavior of both phenotypes of microglia, such as M1 type (pro-inflammatory) vis-à-vis M2 type (anti-inflammatory) under anti-inflammatory drug action (minocycline). We explore whether pharmacological treatment along with cerebral stimulation using tDCS and rTMS is beneficial or not. We utilize the systems pathway analysis of minocycline in nuclear factor kappa beta (NF-κB) signaling and neurorehabilitation therapy using tDCS and rTMS that act through brain-derived neurotrophic factor (BDNF) and tropomyosin-related kinase B (TrkB) signaling pathways. RESULTS We demarcate the role of neuroinflammation and immunomodulation in post-stroke recovery, under minocycline activated-microglia and neuroprotection together with improved neurogenesis, synaptogenesis, and functional recovery under the action of rTMS or tDCS. We elucidate the feasibility of utilizing rTMS/tDCS to increase neuroprotection across the reperfusion stage during minocycline administration. We delineate that the signaling pathways of minocycline by modulation of inflammatory genes in NF-κB and proteins activated by tDCS and rTMS through BDNF, TrkB, and calmodulin kinase (CaMK) signaling. Utilizing systems biology approach, we show that the activation pathways for pharmacotherapy (minocycline) and neurorehabilitation (rTMS applied to ipsilesional cortex and tDCS) results into increased neuronal and synaptic activity that commonly occur through activation of N-methyl-d-aspartate receptors. We construe that considerable additive neuroprotection effect would be obtained and delayed reperfusion injury can be remedied, if one uses multimodal intervention of minocycline together with tDCS and rTMS. CONCLUSION Additive beneficial effect is, thus, noticed for pharmacotherapy along with neurorehabilitation therapy, by maneuvering the dynamics of immunomodulation using anti-inflammatory drug and cerebral stimulation for augmenting the functional recovery after stroke, which may engender clinical applicability for enhancing plasticity, rehabilitation, and neurorestoration.
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Affiliation(s)
| | | | - Prasun K Roy
- National Brain Research Centre , Gurgaon , India
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40
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Pollmächer J, Timme S, Schuster S, Brakhage AA, Zipfel PF, Figge MT. Deciphering the Counterplay of Aspergillus fumigatus Infection and Host Inflammation by Evolutionary Games on Graphs. Sci Rep 2016; 6:27807. [PMID: 27291424 PMCID: PMC4904243 DOI: 10.1038/srep27807] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 05/20/2016] [Indexed: 11/09/2022] Open
Abstract
Microbial invaders are ubiquitously present and pose the constant risk of infections that are opposed by various defence mechanisms of the human immune system. A tight regulation of the immune response ensures clearance of microbial invaders and concomitantly limits host damage that is crucial for host viability. To investigate the counterplay of infection and inflammation, we simulated the invasion of the human-pathogenic fungus Aspergillus fumigatus in lung alveoli by evolutionary games on graphs. The layered structure of the innate immune system is represented by a sequence of games in the virtual model. We show that the inflammatory cascade of the immune response is essential for microbial clearance and that the inflammation level correlates with the infection-dose. At low infection-doses, corresponding to daily inhalation of conidia, the resident alveolar macrophages may be sufficient to clear infections, however, at higher infection-doses their primary task shifts towards recruitment of neutrophils to infection sites.
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Affiliation(s)
- Johannes Pollmächer
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany
- Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Germany
| | - Sandra Timme
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany
- Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Germany
| | - Stefan Schuster
- Department of Bioinformatics, Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Germany
| | - Axel A. Brakhage
- Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Germany
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany
| | - Peter F. Zipfel
- Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Germany
- Department of Infection Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany
| | - Marc Thilo Figge
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany
- Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Germany
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Iwashyna TJ, Burke JF, Sussman JB, Prescott HC, Hayward RA, Angus DC. Implications of Heterogeneity of Treatment Effect for Reporting and Analysis of Randomized Trials in Critical Care. Am J Respir Crit Care Med 2016; 192:1045-51. [PMID: 26177009 DOI: 10.1164/rccm.201411-2125cp] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Randomized clinical trials (RCTs) are conducted to guide clinicians' selection of therapies for individual patients. Currently, RCTs in critical care often report an overall mean effect and selected individual subgroups. Yet work in other fields suggests that such reporting practices can be improved. Specifically, this Critical Care Perspective reviews recent work on so-called "heterogeneity of treatment effect" (HTE) by baseline risk and extends that work to examine its applicability to trials of acute respiratory failure and severe sepsis. Because patients in RCTs in critical care medicine-and patients in intensive care units-have wide variability in their risk of death, these patients will have wide variability in the absolute benefit that they can derive from a given therapy. If the side effects of the therapy are not perfectly collinear with the treatment benefits, this will result in HTE, where different patients experience quite different expected benefits of a therapy. We use simulations of RCTs to demonstrate that such HTE could result in apparent paradoxes, including: (1) positive trials of therapies that are beneficial overall but consistently harm or have little benefit to low-risk patients who met enrollment criteria, and (2) overall negative trials of therapies that still consistently benefit high-risk patients. We further show that these results persist even in the presence of causes of death unmodified by the treatment under study. These results have implications for reporting and analyzing RCT data, both to better understand how our therapies work and to improve the bedside applicability of RCTs. We suggest a plan for measurement in future RCTs in the critically ill.
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Affiliation(s)
- Theodore J Iwashyna
- 1 Department of Internal Medicine and.,2 Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,3 Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Health System, Ann Arbor, Michigan; and
| | - James F Burke
- 4 Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Jeremy B Sussman
- 1 Department of Internal Medicine and.,3 Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Health System, Ann Arbor, Michigan; and
| | | | - Rodney A Hayward
- 1 Department of Internal Medicine and.,3 Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Health System, Ann Arbor, Michigan; and
| | - Derek C Angus
- 5 Clinical Research, Investigation, and Systems Modeling of Acute Illness Laboratory, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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Brown D, Namas RA, Almahmoud K, Zaaqoq A, Sarkar J, Barclay DA, Yin J, Ghuma A, Abboud A, Constantine G, Nieman G, Zamora R, Chang SC, Billiar TR, Vodovotz Y. Trauma in silico: Individual-specific mathematical models and virtual clinical populations. Sci Transl Med 2016; 7:285ra61. [PMID: 25925680 DOI: 10.1126/scitranslmed.aaa3636] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
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.
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Affiliation(s)
| | - Rami A Namas
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Khalid Almahmoud
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Akram Zaaqoq
- Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | | | - Derek A Barclay
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jinling Yin
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ali Ghuma
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Andrew Abboud
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Gregory Constantine
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Gary Nieman
- Department of Surgery, Upstate Medical University, Syracuse, NY 13210, USA
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA 15219, USA
| | | | - Timothy R Billiar
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA 15219, USA.
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O'Leary DP, Zaheer A, Redmond HP, Corrigan MA. Integration of advances in social media and mHealth technology are pivotal to successful cancer prevention and control. Mhealth 2016; 2:38. [PMID: 28293611 PMCID: PMC5344161 DOI: 10.21037/mhealth.2016.09.02] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/22/2016] [Indexed: 01/09/2023] Open
Abstract
The successful prevention and treatment of cancer is dependent upon efficient and reliable communication between healthcare workers and patients. Advances in social media and mHealth platforms have provided new ways in which to enhance the sharing of cancer related information. Other benefits of embracing this technology include utilising its analytic capabilities which can process the vast quantity of information generated from genome exploration in a highly efficient manner. The aim of this review is to provide an overview of the rapidly evolving areas through which digital engagement is proving useful in the prevention and control of cancer.
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Affiliation(s)
- D Peter O'Leary
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - Amir Zaheer
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - H Paul Redmond
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - Mark A Corrigan
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
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Vodovotz Y. Reverse Engineering the Inflammatory "Clock": From Computational Modeling to Rational Resetting. ACTA ACUST UNITED AC 2016; 22:57-63. [PMID: 29333176 DOI: 10.1016/j.ddmod.2017.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219
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Namas RA, Mi Q, Namas R, Almahmoud K, Zaaqoq AM, Abdul-Malak O, Azhar N, Day J, Abboud A, Zamora R, Billiar TR, Vodovotz Y. Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation. Antioxid Redox Signal 2015; 23:1370-87. [PMID: 26560096 PMCID: PMC4685502 DOI: 10.1089/ars.2015.6398] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
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.
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Affiliation(s)
- Rami A. Namas
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Qi Mi
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Rajaie Namas
- Department of Internal Medicine, Division of Rheumatology, University of Michigan, Ann Arbor, Michigan
| | - Khalid Almahmoud
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Akram M. Zaaqoq
- Department of Critical Care Medicine, University of Pittsburgh, Pennsylvania
| | - Othman Abdul-Malak
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Nabil Azhar
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Judy Day
- Department of Mathematics, University of Tennessee, Knoxville, Tennessee
| | - Andrew Abboud
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Timothy R. Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
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46
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Day JD, Metes DM, Vodovotz Y. Mathematical Modeling of Early Cellular Innate and Adaptive Immune Responses to Ischemia/Reperfusion Injury and Solid Organ Allotransplantation. Front Immunol 2015; 6:484. [PMID: 26441988 PMCID: PMC4585194 DOI: 10.3389/fimmu.2015.00484] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 09/07/2015] [Indexed: 12/22/2022] Open
Abstract
A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression.
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Affiliation(s)
- Judy D. Day
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
- National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA
| | - Diana M. Metes
- Department of Surgery and Immunology, Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA, USA
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47
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Ziraldo C, Solovyev A, Allegretti A, Krishnan S, Henzel MK, Sowa GA, Brienza D, An G, Mi Q, Vodovotz Y. A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury. PLoS Comput Biol 2015; 11:e1004309. [PMID: 26111346 PMCID: PMC4482429 DOI: 10.1371/journal.pcbi.1004309] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Accepted: 04/30/2015] [Indexed: 12/22/2022] Open
Abstract
People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to "better" vs. "worse" outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU.
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Affiliation(s)
- Cordelia Ziraldo
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Joint PhD Program in Computational Biology, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Alexey Solovyev
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ana Allegretti
- Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Shilpa Krishnan
- Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - M. Kristi Henzel
- Spinal Cord Injury/Disorders Center, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Gwendolyn A. Sowa
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - David Brienza
- Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Gary An
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Surgery, University of Chicago, Chicago, Illinois, United States of America
| | - Qi Mi
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Yoram Vodovotz
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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48
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Price I, Mochan-Keef ED, Swigon D, Ermentrout GB, Lukens S, Toapanta FR, Ross TM, Clermont G. The inflammatory response to influenza A virus (H1N1): An experimental and mathematical study. J Theor Biol 2015; 374:83-93. [PMID: 25843213 DOI: 10.1016/j.jtbi.2015.03.017] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 03/12/2015] [Accepted: 03/13/2015] [Indexed: 10/23/2022]
Abstract
Mortality from influenza infections continues as a global public health issue, with the host inflammatory response contributing to fatalities related to the primary infection. Based on Ordinary Differential Equation (ODE) formalism, a computational model was developed for the in-host response to influenza A virus, merging inflammatory, innate, adaptive and humoral responses to virus and linking severity of infection, the inflammatory response, and mortality. The model was calibrated using dense cytokine and cell data from adult BALB/c mice infected with the H1N1 influenza strain A/PR/8/34 in sublethal and lethal doses. Uncertainty in model parameters and disease mechanisms was quantified using Bayesian inference and ensemble model methodology that generates probabilistic predictions of survival, defined as viral clearance and recovery of the respiratory epithelium. The ensemble recovers the expected relationship between magnitude of viral exposure and the duration of survival, and suggests mechanisms primarily responsible for survival, which could guide the development of immuno-modulatory interventions as adjuncts to current anti-viral treatments. The model is employed to extrapolate from available data survival curves for the population and their dependence on initial viral aliquot. In addition, the model allows us to illustrate the positive effect of controlled inflammation on influenza survival.
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Affiliation(s)
- Ian Price
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ericka D Mochan-Keef
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - David Swigon
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sarah Lukens
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Ted M Ross
- Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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Christley S, Cockrell C, An G. Computational Studies of the Intestinal Host-Microbiota Interactome. COMPUTATION (BASEL, SWITZERLAND) 2015; 3:2-28. [PMID: 34765258 PMCID: PMC8580329 DOI: 10.3390/computation3010002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.
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Affiliation(s)
- Scott Christley
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| | - Chase Cockrell
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| | - Gary An
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
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50
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Vanlandingham SC, Kurz MC, Wang HE. Thermodynamic aspects of therapeutic hypothermia. Resuscitation 2015; 86:67-73. [DOI: 10.1016/j.resuscitation.2014.09.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 09/15/2014] [Accepted: 09/22/2014] [Indexed: 11/26/2022]
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