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Datta M, Kennedy M, Siri S, Via LE, Baish JW, Xu L, Dartois V, Barry CE, Jain RK. Mathematical model of oxygen, nutrient, and drug transport in tuberculosis granulomas. PLoS Comput Biol 2024; 20:e1011847. [PMID: 38335224 PMCID: PMC10883541 DOI: 10.1371/journal.pcbi.1011847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/22/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Physiological abnormalities in pulmonary granulomas-pathological hallmarks of tuberculosis (TB)-compromise the transport of oxygen, nutrients, and drugs. In prior studies, we demonstrated mathematically and experimentally that hypoxia and necrosis emerge in the granuloma microenvironment (GME) as a direct result of limited oxygen availability. Building on our initial model of avascular oxygen diffusion, here we explore additional aspects of oxygen transport, including the roles of granuloma vasculature, transcapillary transport, plasma dilution, and interstitial convection, followed by cellular metabolism. Approximate analytical solutions are provided for oxygen and glucose concentration, interstitial fluid velocity, interstitial fluid pressure, and the thickness of the convective zone. These predictions are in agreement with prior experimental results from rabbit TB granulomas and from rat carcinoma models, which share similar transport limitations. Additional drug delivery predictions for anti-TB-agents (rifampicin and clofazimine) strikingly match recent spatially-resolved experimental results from a mouse model of TB. Finally, an approach to improve molecular transport in granulomas by modulating interstitial hydraulic conductivity is tested in silico.
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Affiliation(s)
- Meenal Datta
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - McCarthy Kennedy
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Saeed Siri
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Laura E Via
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Disease (NIAID), National Institutes of Health, Bethesda, Maryland, United States of America
| | - James W Baish
- Department of Biomedical Engineering, Bucknell University, Lewisburg, Pennsylvania, United States of America
| | - Lei Xu
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Véronique Dartois
- Center for Discovery and Innovation, Hackensack Meridian School of Medicine, Hackensack Meridian Health, Nutley, New Jersey, United States of America
| | - Clifton E Barry
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Disease (NIAID), National Institutes of Health, Bethesda, Maryland, United States of America
| | - Rakesh K Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
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2
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Saula AY, Knight G, Bowness R. Within-Host Mathematical Models of Antibiotic Resistance. Methods Mol Biol 2024; 2833:79-91. [PMID: 38949703 DOI: 10.1007/978-1-0716-3981-8_9] [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] [Indexed: 07/02/2024]
Abstract
Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.
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Affiliation(s)
| | - Gwenan Knight
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, UK.
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3
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Saula AY, Rowlatt C, Bowness R. Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host. Methods Mol Biol 2024; 2833:93-108. [PMID: 38949704 DOI: 10.1007/978-1-0716-3981-8_10] [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] [Indexed: 07/02/2024]
Abstract
To model complex systems, individual-based models (IBMs), sometimes called "agent-based models" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.
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Affiliation(s)
| | | | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, UK.
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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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Li Z, Lai K, Li T, Lin Z, Liang Z, Du Y, Zhang J. Factors associated with treatment outcomes of patients with drug-resistant tuberculosis in China: A retrospective study using competing risk model. Front Public Health 2022; 10:906798. [PMID: 36159235 PMCID: PMC9490188 DOI: 10.3389/fpubh.2022.906798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/15/2022] [Indexed: 01/22/2023] Open
Abstract
Objectives Drug-resistant tuberculosis remains a serious public health problem worldwide, particularly in developing countries, including China. This study determined treatment outcomes among a cohort in Guangzhou, China, and identified factors associated with them. Methods We initiated a retrospective study using drug-resistant TB data in Guangzhou from 2016 to 2020, managed by Guangzhou Chest Hospital. A competing risk model was used to identify the factors associated with treatment failure and death, as well as loss to follow-up (LTFU). Results A total of 809 patients were included in the study, of which 281 were under treatment. Of the remaining 528 who had clear treatment outcomes, the number and proportion of treatment success, treatment failure, death, and LTFU were 314 (59.5%), 14 (2.7%), 32 (6.0%), and 168 (31.8%), respectively. Being older and having cavities involving the upper lungs were risk factors for treatment failure and death, while non-Guangzhou household registration and interprovincial mobility were risk factors associated with LTFU. Conclusion Treatment failure and death were significantly associated with cavitation in the lungs, and LTFU was significantly associated with household registration and geographical mobility. Early identification of factors associated with different treatment outcomes is extremely important for policymakers, health experts, and researchers to implement appropriate strategies and measures to treat and manage the TB-infected population in China.
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Affiliation(s)
- Zhiwei Li
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Keng Lai
- Department of Tuberculosis Control and Prevention, Guangzhou Chest Hospital, Guangzhou, China
| | - Tiegang Li
- Department of Administration of Disease Prevention and Control, Guangzhou Health Committee, Guangzhou, China
| | - Zhuochen Lin
- Department of Medical Records, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zichao Liang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yuhua Du
- Department of Tuberculosis Control and Prevention, Guangzhou Chest Hospital, Guangzhou, China,Yuhua Du
| | - Jinxin Zhang
- Department of Tuberculosis Control and Prevention, Guangzhou Chest Hospital, Guangzhou, China,*Correspondence: Jinxin Zhang
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Karr J, Malik-Sheriff RS, Osborne J, Gonzalez-Parra G, Forgoston E, Bowness R, Liu Y, Thompson R, Garira W, Barhak J, Rice J, Torres M, Dobrovolny HM, Tang T, Waites W, Glazier JA, Faeder JR, Kulesza A. Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:822606. [PMID: 36909847 PMCID: PMC10002468 DOI: 10.3389/fsysb.2022.822606] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.
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Affiliation(s)
- Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rahuman S. Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - James Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia
| | | | - Eric Forgoston
- Department of Applied Mathematics and Statistics, Montclair State University, Montclair, NJ, United States
| | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Robin Thompson
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Winston Garira
- Department of Mathematics and Applied Mathematics, Modelling Health and Environmental Linkages Research Group, University of Venda, Limpopo, South Africa
| | - Jacob Barhak
- Jacob Barhak Analytics, Austin, TX, United States
| | - John Rice
- Independent Retired Working Group Volunteer, Virginia Beach, VA, United States
| | - Marcella Torres
- Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, United States
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, United States
| | - Tingting Tang
- Department of Mathematics and Statistics in San Diego State University (SDSU) and SDSU Imperial Valley, Calexico, CA, United States
| | - William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
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7
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Sharma A, Wood KB. Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress. THE ISME JOURNAL 2021; 15:3019-3033. [PMID: 33953363 PMCID: PMC8443724 DOI: 10.1038/s41396-021-00982-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 03/19/2021] [Accepted: 04/09/2021] [Indexed: 02/01/2023]
Abstract
Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.
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Affiliation(s)
- Anupama Sharma
- Department of Biophysics, University of Michigan, Ann Arbor, USA
- Department of Mathematics, BITS Pilani K K Birla Goa Campus, Goa, India
| | - Kevin B Wood
- Department of Biophysics, University of Michigan, Ann Arbor, USA.
- Department of Physics, University of Michigan, Ann Arbor, USA.
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8
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Cicchese JM, Sambarey A, Kirschner D, Linderman JJ, Chandrasekaran S. A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs. Sci Rep 2021; 11:5643. [PMID: 33707554 PMCID: PMC7971003 DOI: 10.1038/s41598-021-84827-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.
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Affiliation(s)
- Joseph M. Cicchese
- grid.214458.e0000000086837370Department of Chemical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Awanti Sambarey
- grid.214458.e0000000086837370Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Denise Kirschner
- grid.214458.e0000000086837370Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI USA
| | - Jennifer J. Linderman
- grid.214458.e0000000086837370Department of Chemical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Sriram Chandrasekaran
- grid.214458.e0000000086837370Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
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9
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Pitcher MJ, Bowness R, Dobson S, Eftimie R, Gillespie SH. Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle. J Theor Biol 2020; 506:110381. [PMID: 32771534 PMCID: PMC7511696 DOI: 10.1016/j.jtbi.2020.110381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 05/24/2020] [Accepted: 06/15/2020] [Indexed: 11/28/2022]
Abstract
In silico model of TB in the lung incorporating environmental heterogeneity. Preferential conditions at the apex of lung localise post-primary disease there. Analysis of the key influences driving disease at different regions of the lung.
Progress in shortening the duration of tuberculosis (TB) treatment is hampered by the lack of a predictive model that accurately reflects the diverse environment within the lung. This is important as TB has been shown to produce distinct localisations to different areas of the lung during different disease stages, with the environmental heterogeneity within the lung of factors such as air ventilation, blood perfusion and oxygen tension believed to contribute to the apical localisation witnessed during the post-primary form of the disease. Building upon our previous model of environmental lung heterogeneity, we present a networked metapopulation model that simulates TB across the whole lung, incorporating these notions of environmental heterogeneity across the whole TB life-cycle to show how different stages of the disease are influenced by different environmental and immunological factors. The alveolar tissue in the lung is divided into distinct patches, with each patch representing a portion of the total tissue and containing environmental attributes that reflect the internal conditions at that location. We include populations of bacteria and immune cells in various states, and events are included which determine how the members of the model interact with each other and the environment. By allowing some of these events to be dependent on environmental attributes, we create a set of heterogeneous dynamics, whereby the location of the tissue within the lung determines the disease pathological events that occur there. Our results show that the environmental heterogeneity within the lung is a plausible driving force behind the apical localisation during post-primary disease. After initial infection, bacterial levels will grow in the initial infection location at the base of the lung until an adaptive immune response is initiated. During this period, bacteria are able to disseminate and create new lesions throughout the lung. During the latent stage, the lesions that are situated towards the apex are the largest in size, and once a post-primary immune-suppressing event occurs, it is the uppermost lesions that reach the highest levels of bacterial proliferation. Our sensitivity analysis also shows that it is the differential in blood perfusion, causing reduced immune activity towards the apex, which has the biggest influence of disease outputs.
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Affiliation(s)
- Michael J Pitcher
- School of Immunology and Microbial Sciences, King's College London, United Kingdom; School of Computer Science, University of St Andrews, United Kingdom.
| | - Ruth Bowness
- School of Medicine, University of St Andrews, United Kingdom
| | - Simon Dobson
- School of Computer Science, University of St Andrews, United Kingdom
| | - Raluca Eftimie
- School of Science and Engineering, University of Dundee, United Kingdom
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10
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Ernest JP, Strydom N, Wang Q, Zhang N, Nuermberger E, Dartois V, Savic RM. Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis. Annu Rev Pharmacol Toxicol 2020; 61:495-516. [PMID: 32806997 DOI: 10.1146/annurev-pharmtox-030920-011143] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.
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Affiliation(s)
- Jacqueline P Ernest
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Natasha Strydom
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Qianwen Wang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Nan Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Eric Nuermberger
- Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA
| | - Véronique Dartois
- Center for Discovery and Innovation, Hackensack Meridian School of Medicine at Seton Hall University, Nutley, New Jersey 07110, USA
| | - Rada M Savic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
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11
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Català M, Bechini J, Tenesa M, Pérez R, Moya M, Vilaplana C, Valls J, Alonso S, López D, Cardona PJ, Prats C. Modelling the dynamics of tuberculosis lesions in a virtual lung: Role of the bronchial tree in endogenous reinfection. PLoS Comput Biol 2020; 16:e1007772. [PMID: 32433644 PMCID: PMC7239440 DOI: 10.1371/journal.pcbi.1007772] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 03/04/2020] [Indexed: 01/04/2023] Open
Abstract
Tuberculosis (TB) is an infectious disease that still causes more than 1.5 million deaths annually. The World Health Organization estimates that around 30% of the world's population is latently infected. However, the mechanisms responsible for 10% of this reserve (i.e., of the latently infected population) developing an active disease are not fully understood, yet. The dynamic hypothesis suggests that endogenous reinfection has an important role in maintaining latent infection. In order to examine this hypothesis for falsifiability, an agent-based model of growth, merging, and proliferation of TB lesions was implemented in a computational bronchial tree, built with an iterative algorithm for the generation of bronchial bifurcations and tubes applied inside a virtual 3D pulmonary surface. The computational model was fed and parameterized with computed tomography (CT) experimental data from 5 latently infected minipigs. First, we used CT images to reconstruct the virtual pulmonary surfaces where bronchial trees are built. Then, CT data about TB lesion' size and location to each minipig were used in the parameterization process. The model's outcome provides spatial and size distributions of TB lesions that successfully reproduced experimental data, thus reinforcing the role of the bronchial tree as the spatial structure triggering endogenous reinfection. A sensitivity analysis of the model shows that the final number of lesions is strongly related with the endogenous reinfection frequency and maximum growth rate of the lesions, while their mean diameter mainly depends on the spatial spreading of new lesions and the maximum radius. Finally, the model was used as an in silico experimental platform to explore the transition from latent infection to active disease, identifying two main triggering factors: a high inflammatory response and the combination of a moderate inflammatory response with a small breathing amplitude.
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Affiliation(s)
- Martí Català
- Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Catalonia, Spain
- Departament de Física, Universitat Politècnica de Catalunya, Castelldefels, Barcelona, Catalonia, Spain
| | - Jordi Bechini
- Servei de Radiodiagnòstic, Hospital Universitari Germans Trias i Pujol, Badalona, Catalonia, Spain
| | - Montserrat Tenesa
- Servei de Radiodiagnòstic, Hospital Universitari Germans Trias i Pujol, Badalona, Catalonia, Spain
| | - Ricardo Pérez
- Servei de Radiodiagnòstic, Hospital Universitari Germans Trias i Pujol, Badalona, Catalonia, Spain
| | - Mariano Moya
- Servei de Radiodiagnòstic, Hospital Universitari Germans Trias i Pujol, Badalona, Catalonia, Spain
| | - Cristina Vilaplana
- Experimental Tuberculosis Unit, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Universitat Autònoma de Barcelona, Can Ruti Campus, Edifici Mar, Badalona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Madrid, Spain
| | - Joaquim Valls
- Departament de Física, Universitat Politècnica de Catalunya, Castelldefels, Barcelona, Catalonia, Spain
| | - Sergio Alonso
- Departament de Física, Universitat Politècnica de Catalunya, Castelldefels, Barcelona, Catalonia, Spain
| | - Daniel López
- Departament de Física, Universitat Politècnica de Catalunya, Castelldefels, Barcelona, Catalonia, Spain
| | - Pere-Joan Cardona
- Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Catalonia, Spain
- Experimental Tuberculosis Unit, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Universitat Autònoma de Barcelona, Can Ruti Campus, Edifici Mar, Badalona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Madrid, Spain
| | - Clara Prats
- Departament de Física, Universitat Politècnica de Catalunya, Castelldefels, Barcelona, Catalonia, Spain
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Wolnik B, De Baets B. All binary number-conserving cellular automata based on adjacent cells are intrinsically one-dimensional. Phys Rev E 2019; 100:022126. [PMID: 31574760 DOI: 10.1103/physreve.100.022126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Indexed: 06/10/2023]
Abstract
A binary number-conserving cellular automaton is a discrete dynamical system that models the movement of particles in a d-dimensional grid. Each cell of the grid is either empty or contains a particle. In subsequent time steps the particles move between the cells, but in one cell there can be at most one particle at a time. In this paper, the von Neumann neighborhood is considered, which means that in each time step a particle can move to an adjacent cell only. It is proven that regardless of the dimension d, all of these cellular automata are trivial, as they are intrinsically one-dimensional. Thus, for given d, there are only 4d+1 binary number-conserving cellular automata with the von Neumann neighborhood: the identity rule and the shift and traffic rules in each of the 2d possible directions.
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Affiliation(s)
- Barbara Wolnik
- Institute of Mathematics, Faculty of Mathematics, Physics and Informatics, University of Gdańsk, 80-308 Gdańsk, Poland
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Gent, Belgium
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Pitcher MJ, Bowness R, Dobson S, Gillespie SH. A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection. APPLIED NETWORK SCIENCE 2018; 3:33. [PMID: 30839831 PMCID: PMC6214320 DOI: 10.1007/s41109-018-0091-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 08/05/2018] [Indexed: 06/09/2023]
Abstract
Tuberculosis (TB) is an ancient disease that, although curable, still accounts for over 1 million deaths worldwide. Shortening treatment time is an important area of research but is hampered by the lack of models that mimic the full range of human pathology. TB shows distinct localisations during different stages of infection, the reasons for which are poorly understood. Greater understanding of how heterogeneity within the human lung influences disease progression may hold the key to improving treatment efficiency and reducing treatment times. In this work, we present a novel in silico software model which uses a networked metapopulation incorporating both spatial heterogeneity and dissemination possibilities to simulate a TB infection over the whole lung and associated lymphatics. The entire population of bacteria and immune cells is split into a network of patches: members interact within patches and are able to move between them. Patches and edges of the lung network include their own environmental attributes which influence the dynamics of interactions between the members of the subpopulations of the patches and the translocation of members along edges. In this work, we detail the initial findings of a whole-organ model that incorporates distinct spatial heterogeneity features which are not present in standard differential equation approaches to tuberculosis modelling. We show that the inclusion of heterogeneity within the lung landscape when modelling TB disease progression has significant outcomes on the bacterial load present: a greater differential of oxygen, perfusion and ventilation between the apices and the basal regions of the lungs creates micro-environments at the apex that are more preferential for bacteria, due to increased oxygen availability and reduced immune activity, leading to a greater overall bacterial load present once latency is established. These findings suggest that further whole-organ modelling incorporating more sophisticated heterogeneities within the environment and complex lung topologies will provide more insight into the environments in which TB bacteria persist and thus help develop new treatments which are factored towards these environmental conditions.
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Affiliation(s)
- Michael J. Pitcher
- School of Computer Science, University of St Andrews, North Haugh, St Andrews, UK
| | - Ruth Bowness
- School of Medicine, University of St Andrews, North Haugh, St Andrews, UK
| | - Simon Dobson
- School of Computer Science, University of St Andrews, North Haugh, St Andrews, UK
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