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Gazo Hanna E, Younes K, Roufayel R, Khazaal M, Fajloun Z. Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions. Heliyon 2024; 10:e26154. [PMID: 38390063 PMCID: PMC10882044 DOI: 10.1016/j.heliyon.2024.e26154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 01/24/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
The overlap between mechanical engineering and medicine is expanding more and more over the years. Engineers are now using their expertise to design and create functional biomaterials and are continually collaborating with physicians to improve patient health. In this review, we explore the state of scientific knowledge in the areas of biomaterials, biomechanics, nanomechanics, and computational fluid dynamics (CFD) in relation to the pharmaceutical and medical industry. Focusing on current research and breakthroughs, we provide an overview of how these fields are being used to create new technologies for medical treatments of human patients. Barriers and constraints in these fields, as well as ways to overcome them, are also described in this review. Finally, the potential for future advances in biomaterials to fundamentally change the current approach to medicine and biology is also discussed.
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Affiliation(s)
- Eddie Gazo Hanna
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Khaled Younes
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Rabih Roufayel
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Mickael Khazaal
- École Supérieure des Techniques Aéronautiques et de Construction Automobile, ISAE-ESTACA, France
| | - Ziad Fajloun
- Faculty of Sciences 3, Department of Biology, Lebanese University, Campus Michel Slayman Ras Maska, 1352, Tripoli, Lebanon
- Laboratory of Applied Biotechnology (LBA3B), Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, 1300, Tripoli, Lebanon
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2
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Avilés-Rojas N, Hurtado DE. Whole-lung finite-element models for mechanical ventilation and respiratory research applications. Front Physiol 2022; 13:984286. [PMID: 36267590 PMCID: PMC9577367 DOI: 10.3389/fphys.2022.984286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/12/2022] [Indexed: 11/30/2022] Open
Abstract
Mechanical ventilation has been a vital treatment for Covid-19 patients with respiratory failure. Lungs assisted with mechanical ventilators present a wide variability in their response that strongly depends on air-tissue interactions, which motivates the creation of simulation tools to enhance the design of ventilatory protocols. In this work, we aim to create anatomical computational models of the lungs that predict clinically-relevant respiratory variables. To this end, we formulate a continuum poromechanical framework that seamlessly accounts for the air-tissue interaction in the lung parenchyma. Based on this formulation, we construct anatomical finite-element models of the human lungs from computed-tomography images. We simulate the 3D response of lungs connected to mechanical ventilation, from which we recover physiological parameters of high clinical relevance. In particular, we provide a framework to estimate respiratory-system compliance and resistance from continuum lung dynamic simulations. We further study our computational framework in the simulation of the supersyringe method to construct pressure-volume curves. In addition, we run these simulations using several state-of-the-art lung tissue models to understand how the choice of constitutive models impacts the whole-organ mechanical response. We show that the proposed lung model predicts physiological variables, such as airway pressure, flow and volume, that capture many distinctive features observed in mechanical ventilation and the supersyringe method. We further conclude that some constitutive lung tissue models may not adequately capture the physiological behavior of lungs, as measured in terms of lung respiratory-system compliance. Our findings constitute a proof of concept that finite-element poromechanical models of the lungs can be predictive of clinically-relevant variables in respiratory medicine.
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Affiliation(s)
- Nibaldo Avilés-Rojas
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Daniel E. Hurtado
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
- *Correspondence: Daniel E. Hurtado,
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3
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Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Dieffenbach PB, Aravamudhan A, Fredenburgh LE, Tschumperlin DJ. The Mechanobiology of Vascular Remodeling in the Aging Lung. Physiology (Bethesda) 2022; 37:28-38. [PMID: 34514871 PMCID: PMC8742727 DOI: 10.1152/physiol.00019.2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Aging is accompanied by declining lung function and increasing susceptibility to lung diseases. The role of endothelial dysfunction and vascular remodeling in these changes is supported by growing evidence, but underlying mechanisms remain elusive. In this review we summarize functional, structural, and molecular changes in the aging pulmonary vasculature and explore how interacting aging and mechanobiological cues may drive progressive vascular remodeling in the lungs.
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Affiliation(s)
- Paul B. Dieffenbach
- 1Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Aja Aravamudhan
- 2Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
| | - Laura E. Fredenburgh
- 1Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Daniel J. Tschumperlin
- 2Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
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Maghsoudi-Ganjeh M, Mariano CA, Sattari S, Arora H, Eskandari M. Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework. Front Bioeng Biotechnol 2021; 9:684778. [PMID: 34765590 PMCID: PMC8576180 DOI: 10.3389/fbioe.2021.684778] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 10/04/2021] [Indexed: 02/02/2023] Open
Abstract
Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung’s complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. In the age of a respiratory pandemic, advancing our understanding of lung biomechanics is more pressing than ever before.
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Affiliation(s)
- Mohammad Maghsoudi-Ganjeh
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
| | - Crystal A Mariano
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
| | - Samaneh Sattari
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
| | - Hari Arora
- Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom
| | - Mona Eskandari
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States.,BREATHE Center, School of Medicine, University of California, Riverside, Riverside, CA, United States.,Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
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Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model. Comput Biol Med 2021; 141:105022. [PMID: 34801244 DOI: 10.1016/j.compbiomed.2021.105022] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND OBJECTIVE Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks. METHODS This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH2O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH2O ahead covering 6x2cmH2O PEEP steps. RESULTS Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH2O, compared with 4.76cmH2O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R2 = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH2O. Predicted dynamic functional residual capacity (Vfrc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R2 of 0.84. CONCLUSIONS Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.
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7
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Knopp JL, Chase JG, Kim KT, Shaw GM. Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106300. [PMID: 34348200 DOI: 10.1016/j.cmpb.2021.106300] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/17/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort. METHODS Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH2O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results. RESULTS Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH2O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH2O, with range -7.9 - -1.9 cmH2O. Per-patient E and R were 16.4 [13.6-21.8] cmH2O/L and 9.2 [6.4-13.1] cmH2O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R2=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure. CONCLUSIONS The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.
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Affiliation(s)
- Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Kyeong Tae Kim
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand
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8
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Rampadarath AK, Donovan GM. Mathematical modelling of lung function — what have we learnt and where to next? CURRENT OPINION IN PHYSIOLOGY 2021. [DOI: 10.1016/j.cophys.2021.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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9
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Clark AR, Burrowes KS, Tawhai MH. Integrative Computational Models of Lung Structure-Function Interactions. Compr Physiol 2021; 11:1501-1530. [PMID: 33577123 DOI: 10.1002/cphy.c200011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.
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Affiliation(s)
- Alys R Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kelly S Burrowes
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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10
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Zhou C, Chase JG, Knopp J, Sun Q, Tawhai M, Möller K, Heines SJ, Bergmans DC, Shaw GM, Desaive T. Virtual patients for mechanical ventilation in the intensive care unit. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105912. [PMID: 33360683 DOI: 10.1016/j.cmpb.2020.105912] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/12/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV. METHODS An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers. RESULTS Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc errors are relatively small. CONCLUSIONS Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.
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Affiliation(s)
- Cong Zhou
- School of Civil Aviation, Northwestern Polytechnical University, China; Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - Jennifer Knopp
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Qianhui Sun
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Merryn Tawhai
- Auckland Bio-Engineering Institute (ABI), University of Auckland, New Zealand
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Serge J Heines
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, the Netherlands
| | - Dennis C Bergmans
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, the Netherlands
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In Silico Medicine, Institute of Physics, University of Liege, Liege, Belgium
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11
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Morton SE, Knopp JL, Tawhai MH, Docherty P, Heines SJ, Bergmans DC, Möller K, Chase JG. Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105696. [PMID: 32798977 DOI: 10.1016/j.cmpb.2020.105696] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/30/2020] [Indexed: 06/11/2023]
Abstract
Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH2O. Prediction errors for peak inspiratory volume for an increase of 16 cmH2O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care.
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Affiliation(s)
- Sophie E Morton
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L Knopp
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, Auckland University, Auckland, New Zealand
| | - Paul Docherty
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
| | - Serge J Heines
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
| | - Dennis C Bergmans
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - J Geoffrey Chase
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand.
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12
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Howe SL, Chase JG, Redmond DP, Morton SE, Kim KT, Pretty C, Shaw GM, Tawhai MH, Desaive T. Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 186:105184. [PMID: 31715280 DOI: 10.1016/j.cmpb.2019.105184] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/29/2019] [Accepted: 11/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Model-based lung mechanics monitoring can provide clinically useful information for guiding mechanical ventilator treatment in intensive care. However, many methods of measuring lung mechanics are not appropriate for both fully and partially sedated patients, and are unable provide lung mechanics metrics in real-time. This study proposes a novel method of using lung mechanics identified during passive expiration to estimate inspiratory lung mechanics for spontaneously breathing patients. METHODS Relationships between inspiratory and expiratory modeled lung mechanics were identified from clinical data from 4 fully sedated patients. The validity of these relationships were assessed using data from a further 4 spontaneously breathing patients. RESULTS For the fully sedated patients, a linear relationship was identified between inspiratory and expiratory elastance, with slope 1.04 and intercept 1.66. The r value of this correlation was 0.94. No cohort-wide relationship was determined for airway resistance. Expiratory elastance measurements in spontaneously breathing patients were able to produce reasonable estimates of inspiratory elastance after adjusting for the identified difference between them. CONCLUSIONS This study shows that when conventional methods fail, typically ignored expiratory data may be able to provide clinicians with the information needed about patient condition to guide MV therapy.
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Affiliation(s)
- S L Howe
- University of Canterbury, 8041, Christchurch, New Zealand.
| | - J G Chase
- University of Canterbury, 8041, Christchurch, New Zealand
| | - D P Redmond
- University of Canterbury, 8041, Christchurch, New Zealand
| | - S E Morton
- University of Canterbury, 8041, Christchurch, New Zealand
| | - K T Kim
- University of Canterbury, 8041, Christchurch, New Zealand
| | - C Pretty
- University of Canterbury, 8041, Christchurch, New Zealand
| | - G M Shaw
- Christchurch Hospital, 8011, Christchurch, New Zealand
| | - M H Tawhai
- University of Auckland, Auckland, New Zealand
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13
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Chen Y, Yuan Y, Zhang H, Li F. Comparison of Inspiratory Effort, Workload and Cycling Synchronization Between Non-Invasive Proportional-Assist Ventilation and Pressure-Support Ventilation Using Different Models of Respiratory Mechanics. Med Sci Monit 2019; 25:9048-9057. [PMID: 31778366 PMCID: PMC6900923 DOI: 10.12659/msm.914629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Background This study assessed lung models for the influence of respiratory mechanics and inspiratory effort on breathing pattern and simulator-ventilator cycling synchronization in non-invasive ventilation. Material/Methods A Respironics V60 ventilator was connected to an active lung simulator modeling mildly restrictive, severely restrictive, obstructive and mixed obstructive/restrictive profiles. Pressure-support ventilation (PSV) and proportional-assist ventilation (PAV) were set to obtain similar tidal volume (VT). PAV was applied at flow assist (FA) 40–90% of resistance (Rrs) and volume assist (VA) 40–90% of elastance (Ers). Measurements were performed with system air leak of 25–28 L/minute. Ventilator performance and simulator-ventilator asynchrony were evaluated. Results At comparable VT, PAV had slightly lower peak inspiratory flow and higher driving pressure compared with PSV. Premature cycling occurred in the obstructive, severely restrictive and mildly restrictive models. During PAV, time for airway pressure to achieve 90% of maximum during inspiration (T90) in the severely restrictive model was shorter than those of the obstructive and mixed obstructive/restrictive models and close to that measured in the PSV mode. Increasing FA level reduced inspiratory trigger workload (PTP300) in obstructive and mixed obstructive/restrictive models. Increasing FA level decreased inspiratory time (TI) and tended to aggravate premature cycling, whereas increasing VA level attenuated this effect. Conclusions PAV with an appropriate combination of FA and VA decreases work of breathing during the inspiratory phase and improves simulator-ventilator cycling synchrony. FA has greater impact than VA in the adaptation to inspiratory effort demand. High VA level might help improve cycling synchrony.
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Affiliation(s)
- Yuqing Chen
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China (mainland)
| | - Yueyang Yuan
- School of Mechanical and Electrical Engineering, Hu Nan City University, Yi Yang, Hunan, China (mainland)
| | - Hai Zhang
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China (mainland)
| | - Feng Li
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China (mainland)
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14
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Morton SE, Knopp JL, Chase JG, Docherty P, Howe SL, Möller K, Shaw GM, Tawhai M. Optimising mechanical ventilation through model-based methods and automation. ANNUAL REVIEWS IN CONTROL 2019; 48:369-382. [PMID: 36911536 PMCID: PMC9985488 DOI: 10.1016/j.arcontrol.2019.05.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 04/09/2019] [Accepted: 05/01/2019] [Indexed: 06/11/2023]
Abstract
Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.
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Affiliation(s)
- Sophie E Morton
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Paul Docherty
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Sarah L Howe
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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15
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Donovan GM, Elliot JG, Green FHY, James AL, Noble PB. Unraveling a Clinical Paradox: Why Does Bronchial Thermoplasty Work in Asthma? Am J Respir Cell Mol Biol 2019; 59:355-362. [PMID: 29668295 DOI: 10.1165/rcmb.2018-0011oc] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Bronchial thermoplasty is a relatively new but seemingly effective treatment in subjects with asthma who do not respond to conventional therapy. Although the favored mechanism is ablation of the airway smooth muscle layer, because bronchial thermoplasty treats only a small number of central airways, there is ongoing debate regarding its precise method of action. Our aim in the present study was to elucidate the underlying method of action behind bronchial thermoplasty. We employed a combination of extensive human lung specimens and novel computational methods. Whole left lungs were acquired from the Prairie Provinces Fatal Asthma Study. Subjects were classified as control (n = 31), nonfatal asthma (n = 32), or fatal asthma (n = 25). Simulated lungs for each group were constructed stochastically, and flow distributions and functional indicators (e.g., resistance) were quantified both before and after a 75% reduction in airway smooth muscle in the "thermoplasty-treated" airways. Bronchial thermoplasty triggered global redistribution of clustered flow patterns wherein structural changes to the treated central airways led to a reopening cascade in the small airways and significant improvement in lung function via reduced spatial heterogeneity of flow patterns. This mechanism accounted for progressively greater efficacy of thermoplasty with both severity of asthma and degree of muscle activation, broadly consistent with existing clinical findings. We report a probable mechanism of action for bronchial thermoplasty: alteration of lung-wide flow patterns in response to structural alteration of the treated central airways. This insight could lead to improved therapy via patient-specific, tailored versions of the treatment-as well as to implications for more conventional asthma therapies.
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Affiliation(s)
- Graham M Donovan
- 1 Department of Mathematics, University of Auckland, Auckland, New Zealand
| | - John G Elliot
- 2 West Australian Sleep Disorders Research Institute, Department of Pulmonary Physiology and Sleep Medicine, and
| | - Francis H Y Green
- 3 Airway Inflammation Group, Snyder Institute of Chronic Diseases, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada; and
| | - Alan L James
- 2 West Australian Sleep Disorders Research Institute, Department of Pulmonary Physiology and Sleep Medicine, and.,4 Busselton Population Medical Research Institute, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
| | - Peter B Noble
- 5 School of Human Sciences and.,6 Centre for Neonatal Research and Education, School of Paediatrics and Child Health, The University of Western Australia, Subiaco, Western Australia, Australia
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16
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Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function. Ann Biomed Eng 2019; 47:1626-1641. [PMID: 30927170 DOI: 10.1007/s10439-019-02253-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/22/2019] [Indexed: 10/27/2022]
Abstract
Mechanical ventilation is a life-support therapy for intensive care patients suffering from respiratory failure. To reduce the current rate of ventilator-induced lung injury requires ventilator settings that are patient-, time-, and disease-specific. A common lung protective strategy is to optimise the level of positive end-expiratory pressure (PEEP) through a recruitment manoeuvre to prevent alveolar collapse at the end of expiration and to improve gas exchange through recruitment of additional alveoli. However, this process can subject parts of the lung to excessively high pressures or volumes. This research significantly extends and more robustly validates a previously developed pulmonary mechanics model to predict lung mechanics throughout recruitment manoeuvres. In particular, the process of recruitment is more thoroughly investigated and the impact of the inclusion of expiratory data when estimating peak inspiratory pressure is assessed. Data from the McREM trial and CURE pilot trial were used to test model predictive capability and assumptions. For PEEP changes of up to and including 14 cmH2O, the parabolic model was shown to improve peak inspiratory pressure prediction resulting in less than 10% absolute error in the CURE cohort and 16% in the McREM cohort. The parabolic model also better captured expiratory mechanics than the exponential model for both cohorts.
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17
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Major VJ, Chiew YS, Shaw GM, Chase JG. Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation. Biomed Eng Online 2018; 17:169. [PMID: 30419903 PMCID: PMC6233601 DOI: 10.1186/s12938-018-0599-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 11/01/2018] [Indexed: 12/16/2022] Open
Abstract
Background Mechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO2) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective. Discussion This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions. Conclusion Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.
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Affiliation(s)
- Vincent J Major
- Department of Population Health, NYU Langone Health, New York, NY, USA.
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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18
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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19
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Larsson DH, Vågberg W, Yaroshenko A, Yildirim AÖ, Hertz HM. High-resolution short-exposure small-animal laboratory x-ray phase-contrast tomography. Sci Rep 2016; 6:39074. [PMID: 27958376 PMCID: PMC5153650 DOI: 10.1038/srep39074] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 11/17/2016] [Indexed: 01/06/2023] Open
Abstract
X-ray computed tomography of small animals and their organs is an essential tool in basic and preclinical biomedical research. In both phase-contrast and absorption tomography high spatial resolution and short exposure times are of key importance. However, the observable spatial resolutions and achievable exposure times are presently limited by system parameters rather than more fundamental constraints like, e.g., dose. Here we demonstrate laboratory tomography with few-ten μm spatial resolution and few-minute exposure time at an acceptable dose for small-animal imaging, both with absorption contrast and phase contrast. The method relies on a magnifying imaging scheme in combination with a high-power small-spot liquid-metal-jet electron-impact source. The tomographic imaging is demonstrated on intact mouse, phantoms and excised lungs, both healthy and with pulmonary emphysema.
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Affiliation(s)
- Daniel H. Larsson
- Department of Applied Physics, KTH Royal Institute of Technology/Albanova, 106 91 Stockholm, Sweden
| | - William Vågberg
- Department of Applied Physics, KTH Royal Institute of Technology/Albanova, 106 91 Stockholm, Sweden
| | - Andre Yaroshenko
- Physik-Department & Institut für Medizintechnik, Technische Universität München, Garching, Germany
| | - Ali Önder Yildirim
- Institute of Lung Biology and Disease, Member of the German Center for Lung Research (DZL), Helmholtz Zentrum München, Neuherberg, Germany
| | - Hans M. Hertz
- Department of Applied Physics, KTH Royal Institute of Technology/Albanova, 106 91 Stockholm, Sweden
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20
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Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol 2016; 5:516-531. [PMID: 27653238 PMCID: PMC5080648 DOI: 10.1002/psp4.12134] [Citation(s) in RCA: 216] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 09/09/2016] [Indexed: 12/17/2022] Open
Abstract
The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.
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Affiliation(s)
- L Kuepfer
- Bayer Technology Services, Leverkusen, Germany
| | - C Niederalt
- Bayer Technology Services, Leverkusen, Germany
| | - T Wendl
- Bayer Technology Services, Leverkusen, Germany
| | | | | | - J Lippert
- Bayer HealthCare, Wuppertal, Germany
| | - M Block
- Bayer Technology Services, Leverkusen, Germany
| | - T Eissing
- Bayer Technology Services, Leverkusen, Germany
| | - D Teutonico
- Bayer Technology Services, Leverkusen, Germany.
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21
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Damanhuri NS, Chiew YS, Othman NA, Docherty PD, Pretty CG, Shaw GM, Desaive T, Chase JG. Assessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:175-185. [PMID: 27208532 DOI: 10.1016/j.cmpb.2016.03.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 02/17/2016] [Accepted: 03/29/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. METHODS Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. RESULTS The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. CONCLUSION A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care.
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Affiliation(s)
- Nor Salwa Damanhuri
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand; Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia.
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand; Monash University, Malaysia.
| | - Nor Azlan Othman
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand; Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia.
| | - Paul D Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.
| | - Christopher G Pretty
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand.
| | | | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.
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22
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Stevens G, Atamturktur S, Lebensohn R, Kaschner G. Experiment-based validation and uncertainty quantification of coupled multi-scale plasticity models. ACTA ACUST UNITED AC 2016. [DOI: 10.1108/mmms-04-2015-0023] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
– Highly anisotropic zirconium is a material used in the cladding of nuclear fuel rods, ensuring containment of the radioactive material within. The complex material structure of anisotropic zirconium requires model developers to replicate not only the macro-scale stresses but also the meso-scale material behavior as the crystal structure evolves; leading to strongly coupled multi-scale plasticity models. Such strongly coupled models can be achieved through partitioned analysis techniques, which couple independently developed constituent models through an iterative exchange of inputs and outputs. Throughout this iterative process, biases, and uncertainties inherent within constituent model predictions are inevitably transferred between constituents either compensating for each other or accumulating during iterations. The paper aims to discuss these issues.
Design/methodology/approach
– A finite element model at the macro-scale is coupled in an iterative manner with a meso-scale viscoplastic self-consistent model, where the former supplies the stress input and latter represents the changing material properties. The authors present a systematic framework for experiment-based validation taking advantage of both separate-effect experiments conducted within each constituent’s domain to calibrate the constituents in their respective scales and integral-effect experiments executed within the coupled domain to test the validity of the coupled system.
Findings
– This framework developed is shown to improve predictive capability of a multi-scale plasticity model of highly anisotropic zirconium.
Originality/value
– For multi-scale models to be implemented to support high-consequence decisions, such as the containment of radioactive material, this transfer of biases and uncertainties must be evaluated to ensure accuracy of the predictions of the coupled model. This framework takes advantage of the transparency of partitioned analysis to reduce the accumulation of errors and uncertainties.
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23
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Jimenez-Carretero D, San Jose Estepar R, Diaz Cacio M, Ledesma-Carbayo MJ. Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms. PLoS One 2016; 11:e0146060. [PMID: 26731653 PMCID: PMC4711718 DOI: 10.1371/journal.pone.0146060] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 12/11/2015] [Indexed: 11/18/2022] Open
Abstract
The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery-vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically. Starting from biological and image-based knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. A dataset of 24 labeled anthropomorphic pulmonary CT phantoms were synthesized with the proposed system. Visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems show good correspondence between real and synthetic lungs (p > 0.05 with low Cohen's d effect size and AUC values), supporting the potentiality of the tool and the usefulness of the generated phantoms in the biomedical image processing field.
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Affiliation(s)
| | - Raul San Jose Estepar
- Applied Chest Imaging Laboratory, Brigham and Womens’ Hospital, Boston, Massachusetts, United States of America
| | - Mario Diaz Cacio
- Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
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24
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Abstract
The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.
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Affiliation(s)
- Lars Kuepfer
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany
- Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany
| | - Andreas Schuppert
- Lehrstuhl für datenbasierte Modellierung in CES, Joint Research Center for Computational Biomedicine, AICES, RWTH Aachen University, Augustinerbach 2, Aachen, 52062, Germany.
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25
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Acosta S, Penny DJ, Rusin CG. An effective model of blood flow in capillary beds. Microvasc Res 2015; 100:40-7. [PMID: 25936622 DOI: 10.1016/j.mvr.2015.04.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Revised: 04/24/2015] [Accepted: 04/24/2015] [Indexed: 12/25/2022]
Abstract
In this article we derive applicable expressions for the macroscopic compliance and resistance of microvascular networks. This work yields a lumped-parameter model to describe the hemodynamics of capillary beds. Our derivation takes into account the multiscale nature of capillary networks, the influence of blood volume and pressure on the effective resistance and compliance, as well as, the nonlinear interdependence between these two properties. As a result, we obtain a simple and useful model to study hypotensive and hypertensive phenomena. We include two implementations of our theory: (i) pulmonary hypertension where the flow resistance is predicted as a function of pulmonary vascular tone. We derive from first-principles the inverse proportional relation between resistance and compliance of the pulmonary tree, which explains why the RC factor remains nearly constant across a population with increasing severity of pulmonary hypertension. (ii) The critical closing pressure in pulmonary hypotension where the flow rate dramatically decreases due to the partial collapse of the capillary bed. In both cases, the results from our proposed model compare accurately with experimental data.
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Affiliation(s)
- Sebastian Acosta
- Department of Pediatrics - Cardiology, Baylor College of Medicine, Houston TX, USA; Department of Pediatric Medicine - Cardiology, Texas Children's Hospital, Houston TX, USA.
| | - Daniel J Penny
- Department of Pediatrics - Cardiology, Baylor College of Medicine, Houston TX, USA; Department of Pediatric Medicine - Cardiology, Texas Children's Hospital, Houston TX, USA.
| | - Craig G Rusin
- Department of Pediatrics - Cardiology, Baylor College of Medicine, Houston TX, USA; Department of Pediatric Medicine - Cardiology, Texas Children's Hospital, Houston TX, USA.
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26
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Chiew YS, Pretty C, Docherty PD, Lambermont B, Shaw GM, Desaive T, Chase JG. Time-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing. PLoS One 2015; 10:e0114847. [PMID: 25611069 PMCID: PMC4303416 DOI: 10.1371/journal.pone.0114847] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 11/14/2014] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. METHODS A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison. RESULTS At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator. CONCLUSION The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.
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Affiliation(s)
- Yeong Shiong Chiew
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- * E-mail:
| | - Christopher Pretty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Paul D. Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | | | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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27
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Ma B, Bates JHT. Mechanical interactions between adjacent airways in the lung. J Appl Physiol (1985) 2014; 116:628-34. [PMID: 24481963 DOI: 10.1152/japplphysiol.01180.2013] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The forces of mechanical interdependence between the airways and the parenchyma in the lung are powerful modulators of airways responsiveness. Little is known, however, about the extent to which adjacent airways affect each other's ability to narrow due to distortional forces generated within the intervening parenchyma. We developed a two-dimensional computational model of two airways embedded in parenchyma. The parenchyma itself was modeled in three ways: 1) as a network of hexagonally arranged springs, 2) as a network of triangularly arranged springs, and 3) as an elastic continuum. In all cases, we determined how the narrowing of one airway was affected when the other airway was relaxed vs. when it narrowed to the same extent as the first airway. For the continuum and triangular network models, interactions between airways were negligible unless the airways lay within about two relaxed diameters of each other, but even at this distance the interactions were small. By contrast, the hexagonal spring network model predicted that airway-airway interactions mediated by the parenchyma can be substantial for any degree of airway separation at intermediate values of airway contraction forces. Evidence to date suggests that the parenchyma may be better represented by the continuum model, which suggests that the parenchyma does not mediate significant interactions between narrowing airways.
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Affiliation(s)
- Baoshun Ma
- Department of Medicine, University of Vermont College of Medicine, Burlington, Vermont
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Burrowes KS, De Backer J, Smallwood R, Sterk PJ, Gut I, Wirix-Speetjens R, Siddiqui S, Owers-Bradley J, Wild J, Maier D, Brightling C. Multi-scale computational models of the airways to unravel the pathophysiological mechanisms in asthma and chronic obstructive pulmonary disease (AirPROM). Interface Focus 2014; 3:20120057. [PMID: 24427517 DOI: 10.1098/rsfs.2012.0057] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
THE RESPIRATORY SYSTEM COMPRISES SEVERAL SCALES OF BIOLOGICAL COMPLEXITY: the genes, cells and tissues that work in concert to generate resultant function. Malfunctions of the structure or function of components at any spatial scale can result in diseases, to the detriment of gas exchange, right heart function and patient quality of life. Vast amounts of data emerge from studies across each of the biological scales; however, the question remains: how can we integrate and interpret these data in a meaningful way? Respiratory disease presents a huge health and economic burden, with the diseases asthma and chronic obstructive pulmonary disease (COPD) affecting over 500 million people worldwide. Current therapies are inadequate owing to our incomplete understanding of the disease pathophysiology and our lack of recognition of the enormous disease heterogeneity: we need to characterize this heterogeneity on a patient-specific basis to advance healthcare. In an effort to achieve this goal, the AirPROM consortium (Airway disease Predicting Outcomes through patient-specific computational Modelling) brings together a multi-disciplinary team and a wealth of clinical data. Together we are developing an integrated multi-scale model of the airways in order to unravel the complex pathophysiological mechanisms occurring in the diseases asthma and COPD.
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Affiliation(s)
- K S Burrowes
- Department of Computer Science , University of Oxford , Parks Road, Oxford OX1 3QD , UK
| | | | - R Smallwood
- Kroto Research Institute , University of Sheffield , Sheffield , UK
| | - P J Sterk
- Academic Medical Centre , University of Amsterdam , Amsterdam , The Netherlands
| | - I Gut
- Centro Nacional de Analysis Genómica, Fundacio Privada Parc Cientific de Barcelona , Barcelona , Spain
| | | | - S Siddiqui
- Institute for Lung Health , University of Leicester , Leicester , UK
| | - J Owers-Bradley
- School of Physics and Astronomy, University of Nottingham , Nottingham , UK
| | - J Wild
- Unit of Academic Radiology , University of Sheffield , Sheffield , UK
| | - D Maier
- Biomax Informatics AG , Munich , Germany
| | - C Brightling
- Institute for Lung Health , University of Leicester , Leicester , UK
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Li KC, Marcovici P, Phelps A, Potter C, Tillack A, Tomich J, Tridandapani S. Digitization of medicine: how radiology can take advantage of the digital revolution. Acad Radiol 2013; 20:1479-94. [PMID: 24200474 DOI: 10.1016/j.acra.2013.09.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Revised: 09/07/2013] [Accepted: 09/08/2013] [Indexed: 01/10/2023]
Abstract
In the era of medical cost containment, radiologists must continually maintain their actual and perceived value to patients, payers, and referring providers. Exploitation of current and future digital technologies may be the key to defining and promoting radiology's "brand" and assure our continued relevance in providing predictive, preventive, personalized, and participatory medicine. The Association of University of Radiologists Radiology Research Alliance Digitization of Medicine Task Force was formed to explore the opportunities and challenges of the digitization of medicine that are relevant to radiologists, which include the reporting paradigm, computational biology, and imaging informatics. In addition to discussing these opportunities and challenges, we consider how change occurs in medicine, and how change may be effected in medical imaging community. This review article is a summary of the research of the task force and hopefully can be used as a stimulus for further discussions and development of action plans by radiology leaders.
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Affiliation(s)
- King C Li
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Boulevard, Winston-Salem, NC 27157.
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de Bono B, Hunter P. Integrating knowledge representation and quantitative modelling in physiology. Biotechnol J 2013; 7:958-72. [PMID: 22887885 DOI: 10.1002/biot.201100304] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A wealth of potentially shareable resources, such as data and models, is being generated through the study of physiology by computational means. Although in principle the resources generated are reusable, in practice, few can currently be shared. A key reason for this disparity stems from the lack of consistent cataloguing and annotation of these resources in a standardised manner. Here, we outline our vision for applying community-based modelling standards in support of an automated integration of models across physiological systems and scales. Two key initiatives, the Physiome Project and the European contribution - the Virtual Phsysiological Human Project, have emerged to support this multiscale model integration, and we focus on the role played by two key components of these frameworks, model encoding and semantic metadata annotation. We present examples of biomedical modelling scenarios (the endocrine effect of atrial natriuretic peptide, and the implications of alcohol and glucose toxicity) to illustrate the role that encoding standards and knowledge representation approaches, such as ontologies, could play in the management, searching and visualisation of physiology models, and thus in providing a rational basis for healthcare decisions and contributing towards realising the goal of of personalized medicine.
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Affiliation(s)
- Bernard de Bono
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Kuepfer L, Lippert J, Eissing T. Multiscale mechanistic modeling in pharmaceutical research and development. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2013; 736:543-61. [PMID: 22161351 DOI: 10.1007/978-1-4419-7210-1_32] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Discontinuation of drug development projects due to lack of efficacy or adverse events is one of the main cost drivers in pharmaceutical research and development (R&D). Investments have to be written-off and contribute to the total costs of a successful drug candidate receiving marketing authorization and allowing return on invest. A vital risk for pharmaceutical innovator companies is late stage clinical failure since costs for individual clinical trials may exceed the one billion Euro threshold. To guide investment decisions and to safeguard maximum medical benefit and safety for patients recruited in clinical trials, it is therefore essential to understand the clinical consequences of all information and data generated. The complexity of the physiological and pathophysiological processes and the sheer amount of information available overcharge the mental capacity of any human being and prevent a prediction of the success in clinical development. A rigorous integration of knowledge, assumption, and experimental data into computational models promises a significant improvement of the rationalization of decision making in pharmaceutical industry. We here give an overview of the current status of modeling and simulation in pharmaceutical R&D and outline the perspectives of more recent developments in mechanistic modeling. Specific modeling approaches for different biological scales ranging from intracellular processes to whole organism physiology are introduced and an example for integrative multiscale modeling of therapeutic efficiency in clinical oncology trials is showcased.
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Affiliation(s)
- Lars Kuepfer
- Systems Biology and Computational Solutions, Bayer Technology Services GmbH, Building 9115, 51368 Leverkusen, Germany.
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Niklas J, Diaz Ochoa JG, Bucher J, Mauch K. Quantitative Evaluation and Prediction of Drug Effects and Toxicological Risk Using Mechanistic Multiscale Models. Mol Inform 2012; 32:14-23. [DOI: 10.1002/minf.201200043] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2012] [Accepted: 09/21/2012] [Indexed: 01/06/2023]
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Abstract
MOTIVATION Normal development of multicellular organisms is regulated by a highly complex process in which a set of precursor cells proliferate, differentiate and move, forming over time a functioning tissue. To handle their complexity, developmental systems can be studied over distinct scales. The dynamics of each scale is determined by the collective activity of entities at the scale below it. RESULTS I describe a multi-scale computational approach for modeling developmental systems and detail the methodology through a synthetic example of a developmental system that retains key features of real developmental systems. I discuss the simulation of the system as it emerges from cross-scale and intra-scale interactions and describe how an in silico study can be carried out by modifying these interactions in a way that mimics in vivo experiments. I highlight biological features of the results through a comparison with findings in Caenorhabditis elegans germline development and finally discuss about the applications of the approach in real developmental systems and propose future extensions. AVAILABILITY AND IMPLEMENTATION The source code of the model of the synthetic developmental system can be found in www.wisdom.weizmann.ac.il/~yaki/MultiScaleModel. CONTACT yaki.setty@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yaki Setty
- Weizmann Institute of Science, Computer Science and Applied Mathematics, Rehovot, Israel.
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