Multi-scale computational models of the airways to unravel the pathophysiological mechanisms in asthma and chronic obstructive pulmonary disease (AirPROM)

Modeling Realistic Geometries in Human Intrathoracic Airways

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

Computational fluid dynamics of the airways after left-upper pulmonary lobectomy: A case study

Pulmonary lobectomy is the gold standard intervention for lung cancer removal and consists of the complete resection of the affected lung lobe, which, coupled with the re-adaptation of the remaining thoracic structures, decreases the postoperative pulmonary function of the patient. Current clinical practice, based on spirometry and cardiopulmonary exercise tests, does not consider local changes, providing an average at-the-mouth estimation of residual functionality. Computational Fluid Dynamics (CFD) has proved a valuable solution to obtain quantitative and local information about airways airflow dynamics. A CFD investigation was performed on the airway tree of a left-upper pulmonary lobectomy patient, to quantify the effects of the postoperative alterations. The patient-specific bronchial models were reconstructed from pre- and postoperative CT scans. A parametric laryngeal model was merged to the geometries to account for physiological-like inlet conditions. Numerical simulations were performed in Fluent. The postoperative configuration revealed fluid dynamic variations in terms of global velocity (+23%), wall pressure (+48%), and wall shear stress (+39%). Local flow disturbances emerged at the resection site: a high-velocity peak of 4.92 m/s was found at the left-lower lobe entrance, with a local increase of pressure at the suture zone (18 Pa). The magnitude of pressure and secondary flows increased in the trachea and flow dynamics variations were observed also in the contralateral lung, causing altered lobar ventilation. The results confirmed that CFD is a patient-specific approach for a quantitative evaluation of fluid dynamics parameters and local ventilation providing additional information with respect to current clinical approaches.

A porous circulation model of the human brain for in silico clinical trials in ischaemic stroke

The advancement of ischaemic stroke treatment relies on resource-intensive experiments and clinical trials. In order to improve ischaemic stroke treatments, such as thrombolysis and thrombectomy, we target the development of computational tools for in silico trials which can partially replace these animal and human experiments with fast simulations. This study proposes a model that will serve as part of a predictive unit within an in silico clinical trial estimating patient outcome as a function of treatment. In particular, the present work aims at the development and evaluation of an organ-scale microcirculation model of the human brain for perfusion prediction. The model relies on a three-compartment porous continuum approach. Firstly, a fast and robust method is established to compute the anisotropic permeability tensors representing arterioles and venules. Secondly, vessel encoded arterial spin labelling magnetic resonance imaging and clustering are employed to create an anatomically accurate mapping between the microcirculation and large arteries by identifying superficial perfusion territories. Thirdly, the parameter space of the problem is reduced by analysing the governing equations and experimental data. Fourthly, a parameter optimization is conducted. Finally, simulations are performed with the tuned model to obtain perfusion maps corresponding to an open and an occluded (ischaemic stroke) scenario. The perfusion map in the occluded vessel scenario shows promising qualitative agreement with computed tomography images of a patient with ischaemic stroke caused by large vessel occlusion. The results highlight that in the case of vessel occlusion (i) identifying perfusion territories is essential to capture the location and extent of underperfused regions and (ii) anisotropic permeability tensors are required to give quantitatively realistic estimation of perfusion change. In the future, the model will be thoroughly validated against experiments.

Virtual patients for mechanical ventilation in the intensive care unit

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, V_frc 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 V_frc yield R²=0.86 and R²=0.83. Absolute PIP, PIV and V_frc 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.

Structural and functional alterations of the tracheobronchial tree after left upper pulmonary lobectomy for lung cancer

Background

Pulmonary lobectomy has been a well-established curative treatment method for localized lung cancer. After left upper pulmonary lobectomy, the upward displacement of remaining lower lobe causes the distortion or kink of bronchus, which is associated with intractable cough and breathless. However, the quantitative study on structural and functional alterations of the tracheobronchial tree after lobectomy has not been reported. We sought to investigate these alterations using CT imaging analysis and computational fluid dynamics (CFD) method.

Methods

Both preoperative and postoperative CT images of 18 patients who underwent left upper pulmonary lobectomy are collected. After the tracheobronchial tree models are extracted, the angles between trachea and bronchi, the surface area and volume of the tree, and the cross-sectional area of left lower lobar bronchus are investigated. CFD method is further used to describe the airflow characteristics by the wall pressure, airflow velocity, lobar flow rate, etc.

Results

It is found that the angle between the trachea and the right main bronchus increases after operation, but the angle with the left main bronchus decreases. No significant alteration is observed for the surface area or volume of the tree between pre-operation and post-operation. After left upper pulmonary lobectomy, the cross-sectional area of left lower lobar bronchus is reduced for most of the patients (15/18) by 15–75%, especially for 4 patients by more than 50%. The wall pressure, airflow velocity and pressure drop significantly increase after the operation. The flow rate to the right lung increases significantly by 2–30% (but there is no significant difference between each lobe), and the flow rate to the left lung drops accordingly. Many vortices are found in various places with severe distortions.

Conclusions

The favorable and unfavorable adaptive alterations of tracheobronchial tree will occur after left upper pulmonary lobectomy, and these alterations can be clarified through CT imaging and CFD analysis. The severe distortions at left lower lobar bronchus might exacerbate postoperative shortness of breath.

Personalized medicine for patients with COPD: where are we?

Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.

Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology

The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.

Optimising mechanical ventilation through model-based methods and automation

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.

Integrating Clinical and Epidemiologic Data on Allergic Diseases Across Birth Cohorts: A Harmonization Study in the Mechanisms of the Development of Allergy Project

The numbers of international collaborations among birth cohort studies designed to better understand asthma and allergies have increased in the last several years. However, differences in definitions and methods preclude direct pooling of original data on individual participants. As part of the Mechanisms of the Development of Allergy (MeDALL) Project, we harmonized data from 14 birth cohort studies (each with 3-20 follow-up periods) carried out in 9 European countries during 1990-1998 or 2003-2009. The harmonization process followed 6 steps: 1) organization of the harmonization panel; 2) identification of variables relevant to MeDALL objectives (candidate variables); 3) proposal of a definition for each candidate variable (reference definition); 4) assessment of the compatibility of each cohort variable with its reference definition (inferential equivalence) and classification of this inferential equivalence as complete, partial, or impossible; 5) convocation of a workshop to agree on the reference definitions and classifications of inferential equivalence; and 6) preparation and delivery of data through a knowledge management portal. We agreed on 137 reference definitions. The inferential equivalence of 3,551 cohort variables to their corresponding reference definitions was classified as complete, partial, and impossible for 70%, 15%, and 15% of the variables, respectively. A harmonized database was delivered to MeDALL investigators. In asthma and allergy birth cohorts, the harmonization of data for pooled analyses is feasible, and high inferential comparability may be achieved. The MeDALL harmonization approach can be used in other collaborative projects.

Patient-specific targeted bronchial thermoplasty: predictions of improved outcomes with structure-guided treatment

Bronchial thermoplasty is a recent treatment for asthma in which ablative thermal energy is delivered to specific large airways according to clinical guidelines. Therefore, current practice is effectively "blind," as it is not informed by patient-specific data. The present study seeks to establish whether a patient-specific approach based on structural or functional patient data can improve outcomes and/or reduce the number of procedures required for clinical efficacy. We employed a combination of extensive human lung specimens and novel computational methods to predict bronchial thermoplasty outcomes guided by structural or functional data compared with current clinical practice. Response to bronchial thermoplasty was determined from changes in airway responses to strong bronchoconstrictor simulations and flow heterogeneity after one or three simulated thermoplasty procedures. Structure-guided treatment showed significant improvement over current unguided clinical practice, with a single session of structure-guided treatment producing improvements comparable with three sessions of unguided treatment. In comparison, function-guided treatment did not produce a significant improvement over current practice. Structure-guided targeting of bronchial thermoplasty is a promising avenue for improving therapy and reinforces the need for advanced imaging technologies. The functional imaging-guided approach is predicted to be less effective presently, and we make recommendations on how this approach could be improved. NEW & NOTEWORTHY Bronchial thermoplasty is a recent treatment for asthma in which thermal energy is delivered via bronchoscope to specific airways in an effort to directly target airway smooth muscle. Current practice involves the treatment of a standard set of airways, unguided by patient-specific data. We consider the potential for guided treatments, either by functional or structural data from the lung, and show that treatment guided by structural data has the potential to improve clinical practice.

Metabolomics in asthma: where do we stand?

PURPOSE OF REVIEW

Metabolomics has been used to uncover the metabolic signatures of asthma, both for biomarker identification and pathophysiologic mechanisms research. We aimed to review recent advances in this field, published since 2016, and discuss these findings implications to future research and application into clinical practice.

RECENT FINDINGS

Experimental asthma models and clinical studies in both children and adults supported independent metabolic signatures of asthma. Common reported pathways included purine, glycerophospholipid, glutathione, fatty acids, and arginine and proline metabolism. Metabolomics-based studies identified candidate biomarkers related to asthma severity and corticosteroid resistance, and supported the definition of the obesity-related phenotype at the molecular level. A systematic review with meta-analysis and recent prospective studies favored exhaled volatile organic compounds as one of the most promising biomarkers in asthma diagnosis and monitoring.

SUMMARY

Metabolomics has provided unique and novel insights into asthma profiling at the molecular level. Current challenges include procedures standardization and control of potentially confounding variables for external validation. Point-of-care technology developments bring metabolomics closer to clinical practice. In addition to biomarkers identification, relating metabolites to their biologic role will serve as critical foundations for understanding the biology underpinning asthma heterogeneity and for specific-targeted therapies. VIDEO ABSTRACT.

Particle Disposition in the Realistic Airway Tree Models of Subjects with Tracheal Bronchus and COPD

Dispositions of inhalable particles in the human respiratory tract trigger and exacerbate airway inflammatory diseases. However, the particle deposition (PD) in airway of subjects with tracheal bronchus (TB) and chronic obstructive pulmonary diseases (COPD) is unknown. We therefore propose to clarify the disrupted PD associated with TB and COPD using the computational fluid dynamics (CFD) simulation. Totally nine airway tree models are included. Six are extracted from CT images of different individuals (two with TB, two with COPD, and two healthy controls (HC)). The others are the artificially modified models (AMMs) generated by the virtual lesion. Specifically, they are constructed through artificially adding a tracheal bronchus or a stenosis on one HC model. The deposition efficiency (DE) and deposition fraction (DF) in these models are obtained by the Euler-Lagrange approach, analyzed, and compared across models, locations, and particle sizes (0.1-10.0 micrometers). It is found that the PD in models with TB and COPD has been disrupted by the geometrical changes and followed airflow alternations. DE of the tracheal bronchus is higher for TB models. For COPD, the stenosis location determines the effects on DE and DF. Higher DF at the trachea is observed in TB1, TB2, and COPD2 models. DE increases with the particle size, and DE of the terminal bronchi is higher than that of central regions. Combined with AMMs, the CFD simulation using realistic airway models demonstrates disruptions of DP. The methods and findings might help understand the etiology of pulmonary diseases and improve the efficacy of inhaled medicines.

Coupled Immunological and Biomechanical Model of Emphysema Progression

Chronic Obstructive Pulmonary Disease (COPD) is a disabling respiratory pathology, with a high prevalence and a significant economic and social cost. It is characterized by different clinical phenotypes with different risk profiles. Detecting the correct phenotype, especially for the emphysema subtype, and predicting the risk of major exacerbations are key elements in order to deliver more effective treatments. However, emphysema onset and progression are influenced by a complex interaction between the immune system and the mechanical properties of biological tissue. The former causes chronic inflammation and tissue remodeling. The latter influences the effective resistance or appropriate mechanical response of the lung tissue to repeated breathing cycles. In this work we present a multi-scale model of both aspects, coupling Finite Element (FE) and Agent Based (AB) techniques that we would like to use to predict the onset and progression of emphysema in patients. The AB part is based on existing biological models of inflammation and immunological response as a set of coupled non-linear differential equations. The FE part simulates the biomechanical effects of repeated strain on the biological tissue. We devise a strategy to couple the discrete biological model at the molecular /cellular level and the biomechanical finite element simulations at the tissue level. We tested our implementation on a public emphysema image database and found that it can indeed simulate the evolution of clinical image biomarkers during disease progression.

Airflow in Tracheobronchial Tree of Subjects with Tracheal Bronchus Simulated Using CT Image Based Models and CFD Method

Tracheal Bronchus (TB) is a rare congenital anomaly characterized by the presence of an abnormal bronchus originating from the trachea or main bronchi and directed toward the upper lobe. The airflow pattern in tracheobronchial trees of TB subjects is critical, but has not been systemically studied. This study proposes to simulate the airflow using CT image based models and the computational fluid dynamics (CFD) method. Six TB subjects and three health controls (HC) are included. After the geometric model of tracheobronchial tree is extracted from CT images, the spatial distribution of velocity, wall pressure, wall shear stress (WSS) is obtained through CFD simulation, and the lobar distribution of air, flow pattern and global pressure drop are investigated. Compared with HC subjects, the main bronchus angle of TB subjects and the variation of volume are large, while the cross-sectional growth rate is small. High airflow velocity, wall pressure, and WSS are observed locally at the tracheal bronchus, but the global patterns of these measures are still similar to those of HC. The ratio of airflow into the tracheal bronchus accounts for 6.6-15.6% of the inhaled airflow, decreasing the ratio to the right upper lobe from 15.7-21.4% (HC) to 4.9-13.6%. The air into tracheal bronchus originates from the right dorsal near-wall region of the trachea. Tracheal bronchus does not change the global pressure drop which is dependent on multiple variables. Though the tracheobronchial trees of TB subjects present individualized features, several commonalities on the structural and airflow characteristics can be revealed. The observed local alternations might provide new insight into the reason of recurrent local infections, cough and acute respiratory distress related to TB.

Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes

BACKGROUND

Chronic obstructive pulmonary disease (COPD) and asthma are considered as the two most widespread obstructive lung diseases, whereas they affect more than 500 million people worldwide. Unfortunately, the requirement for detailed geometric models of the lungs in combination with the increased computational resources needed for the simulation of the breathing did not allow great progress to be made in the past for the better understanding of inflammatory diseases of the airways through detailed modelling approaches. In this context, computational fluid dynamics (CFD) simulations accompanied by fluid particle tracing (FPT) analysis of the inhaled ambient particles are deemed critical for lung function assessment. Also they enable the understanding of particle depositions on the airways of patients, since these accumulations may affect or lead to inflammations. In this direction, the current study conducts an initial investigation for the better comprehension of particle deposition within the lungs. More specifically, accurate models of the airways obstructions that relate to pulmonary disease are developed and a thorough assessment of the airflow behavior together with identification of the effects of inhaled particle properties, such as size and density, is conducted. Our approach presents a first step towards an effective personalization of pulmonary treatment in regards to the geometric characteristics of the lungs and the in depth understanding of airflows within the airways.

METHODS

A geometry processing technique involving contraction algorithms is established and used to employ the different respiratory arrangements associated with lung related diseases that exhibit airways obstructions. Apart from the normal lung case, two categories of obstructed cases are examined, i.e. models with obstructions in both lungs and models with narrowings in the right lung only. Precise assumptions regarding airflow and deposition fraction (DF) over various sections of the lungs are drawn by simulating these distinct incidents through the finite volume method (FVM) and particularly the CFD and FPT algorithms. Moreover, a detailed parametric analysis clarifies the effects of the particles size and density in terms of regional deposition upon several parts of the pulmonary system. In this manner, the deposition pattern of various substances can be assessed.

RESULTS

For the specific case of the unobstructed lung model most particles are detected on the right lung (48.56% of total, when the air flowrate is 12.6 L/min), a fact that is also true when obstructions arise symmetrically in both lungs (51.45% of total, when the air flowrate is 6.06 L/min and obstructions occur after the second generation). In contrast, when narrowings are developed on the right lung only, most particles are pushed on the left section (68.22% of total, when the air flowrate is 11.2 L/min) indicating that inhaled medication is generally deposited away from the areas of inflammation. This observation is useful when designing medical treatment of lung diseases. Furthermore, particles with diameters from 1 μm to 10 μm are shown to be mainly deposited on the lower airways, whereas particles with diameters of 20 μm and 30 μm are mostly accumulated in the upper airways. As a result, the current analysis indicates increased DF levels in the upper airways when the particle diameter is enlarged. Additionally, when the particles density increases from 1000 Kg/m³ to 2000 Kg/m³, the DF is enhanced on every generation and for all cases investigated herein. The results obtained by our simulations provide an accurate and quantitative estimation of all important parameters involved in lung modeling.

CONCLUSIONS

The treatment of respiratory diseases with inhaled medical substances can be advanced by the clinical use of accurate CFD and FPT simulations and specifically by evaluating the deposition of inhaled particles in a regional oriented perspective in regards to different particle sizes and particle densities. Since a drug with specific characteristics (i.e. particle size and density) exhibits maximum deposition on particular lung areas, the current study provides initial indications to a qualified physician for proper selection of medication.

Transient Dynamics Simulation of Airflow in a CT-Scanned Human Airway Tree: More or Fewer Terminal Bronchi?

Using computational fluid dynamics (CFD) method, the feasibility of simulating transient airflow in a CT-based airway tree with more than 100 outlets for a whole respiratory period is studied, and the influence of truncations of terminal bronchi on CFD characteristics is investigated. After an airway model with 122 outlets is extracted from CT images, the transient airflow is simulated. Spatial and temporal variations of flow velocity, wall pressure, and wall shear stress are presented; the flow pattern and lobar distribution of air are gotten as well. All results are compared with those of a truncated model with 22 outlets. It is found that the flow pattern shows lobar heterogeneity that the near-wall air in the trachea is inhaled into the upper lobe while the center flow enters the other lobes, and the lobar distribution of air is significantly correlated with the outlet area ratio. The truncation decreases airflow to right and left upper lobes and increases the deviation of airflow distributions between inspiration and expiration. Simulating the transient airflow in an airway tree model with 122 bronchi using CFD is feasible. The model with more terminal bronchi decreases the difference between the lobar distributions at inspiration and at expiration.

Capturing complexity in pulmonary system modelling

Respiratory disease is a significant problem worldwide, and it is a problem with increasing prevalence. Pathology in the upper airways and lung is very difficult to diagnose and treat, as response to disease is often heterogeneous across patients. Computational models have long been used to help understand respiratory function, and these models have evolved alongside increases in the resolution of medical imaging and increased capability of functional imaging, advances in biological knowledge, mathematical techniques and computational power. The benefits of increasingly complex and realistic geometric and biophysical models of the respiratory system are that they are able to capture heterogeneity in patient response to disease and predict emergent function across spatial scales from the delicate alveolar structures to the whole organ level. However, with increasing complexity, models become harder to solve and in some cases harder to validate, which can reduce their impact clinically. Here, we review the evolution of complexity in computational models of the respiratory system, including successes in translation of models into the clinical arena. We also highlight major challenges in modelling the respiratory system, while making use of the evolving functional data that are available for model parameterisation and testing.

The Virtual Physiological Human: Ten Years After

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype-phenotype interaction and by a "systemic" nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible-the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

Enabling Anyone to Translate Clinically Relevant Ideas to Therapies

How do we inspire new ideas that could lead to potential treatments for rare or neglected diseases, and allow for serendipity that could help to catalyze them? How many potentially good ideas are lost because they are never tested? What if those ideas could have lead to new therapeutic approaches and major healthcare advances? If a clinician or anyone for that matter, has a new idea they want to test to develop a molecule or therapeutic that they could translate to the clinic, how would they do it without a laboratory or funding? These are not idle theoretical questions but addressing them could have potentially huge economic implications for nations. If we fail to capture the diversity of ideas and test them we may also lose out on the next blockbuster treatments. Many of those involved in the process of ideation may be discouraged and simply not know where to go. We try to address these questions and describe how there are options to raising funding, how even small scale investments can foster preclinical or clinical translation, and how there are several approaches to outsourcing the experiments, whether to collaborators or commercial enterprises. While these are not new or far from complete solutions, they are first steps that can be taken by virtually anyone while we work on other solutions to build a more concrete structure for the "idea-hypothesis testing-proof of concept-translation-breakthrough pathway".

Development and Analysis of Patient-Based Complete Conducting Airways Models

The analysis of high-resolution computed tomography (CT) images of the lung is dependent on inter-subject differences in airway geometry. The application of computational models in understanding the significance of these differences has previously been shown to be a useful tool in biomedical research. Studies using image-based geometries alone are limited to the analysis of the central airways, down to generation 6-10, as other airways are not visible on high-resolution CT. However, airways distal to this, often termed the small airways, are known to play a crucial role in common airway diseases such as asthma and chronic obstructive pulmonary disease (COPD). Other studies have incorporated an algorithmic approach to extrapolate CT segmented airways in order to obtain a complete conducting airway tree down to the level of the acinus. These models have typically been used for mechanistic studies, but also have the potential to be used in a patient-specific setting. In the current study, an image analysis and modelling pipeline was developed and applied to a number of healthy (n = 11) and asthmatic (n = 24) CT patient scans to produce complete patient-based airway models to the acinar level (mean terminal generation 15.8 ± 0.47). The resulting models are analysed in terms of morphometric properties and seen to be consistent with previous work. A number of global clinical lung function measures are compared to resistance predictions in the models to assess their suitability for use in a patient-specific setting. We show a significant difference (p < 0.01) in airways resistance at all tested flow rates in complete airway trees built using CT data from severe asthmatics (GINA 3-5) versus healthy subjects. Further, model predictions of airways resistance at all flow rates are shown to correlate with patient forced expiratory volume in one second (FEV1) (Spearman ρ = -0.65, p < 0.001) and, at low flow rates (0.00017 L/s), FEV1 over forced vital capacity (FEV1/FVC) (ρ = -0.58, p < 0.001). We conclude that the pipeline and anatomical models can be used directly in mechanistic modelling studies and can form the basis for future patient-based modelling studies.

Asthma Bronchiale and Exercise-Induced Bronchoconstriction

Exercising regularly has a wide range of beneficial health effects; in particular, it has been well documented to help in the management of chronic illnesses including asthma. However, in some individuals, exertion can also trigger an exacerbation of asthmatic episodes and subsequent acute attacks of breathlessness, coughing, tightness of the chest and wheezing. This physiological process is called exercise-induced bronchoconstriction (EIB) whereby post-exercise forced expiratory volume in 1 s is reduced by 10-15% from baseline. While EIB is highly prevalent in asthmatics and presents with similar respiratory symptoms, asthma and EIB are not mutually exclusive. The aim of this review is to present a broad overview of both conditions in order to enhance the understanding of the similarities and differences distinguishing them as two separate entities. The pathophysiology and mechanisms underlying asthma are well described with research now focussing on defining phenotypes for targeted management strategies. Conversely, the mechanistic understanding of EIB remains largely under-described. Diagnostic pathways for both are established and similar, as are pharmacologic and non-pharmacologic treatments and management approaches, which have enhanced success with early detection. Given the potential for exacerbation of asthma, exercise avoidance is common but counterproductive as current evidence indicates that it is well tolerated and improves quality of life. Literature supporting the benefit of exercise for EIB sufferers is at present favourable, yet extremely limited; therefore, future research should be directed in this area as well as towards further developing the understanding of the pathophysiology and mechanisms underpinning both EIB and asthma.

In Vitro Testing for Orally Inhaled Products: Developments in Science-Based Regulatory Approaches

This article is part of a series of reports from the "Orlando Inhalation Conference-Approaches in International Regulation" which was held in March 2014, and coorganized by the University of Florida and the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS). The goal of the conference was to foster the exchange of ideas and knowledge across the global scientific and regulatory community in order to identify and help move towards strategies for internationally harmonized, science-based regulatory approaches for the development and marketing approval of inhalation medicines, including innovator and second entry products. This article provides an integrated perspective of case studies and discussion related to in vitro testing of orally inhaled products, including in vitro-in vivo correlations and requirements for in vitro data and statistical analysis that support quality or bioequivalence for regulatory applications.

Clinical significance of small airway obstruction markers in patients with asthma

BACKGROUND

The role of small airway obstruction in the clinical expression of asthma is incompletely understood.

OBJECTIVE

We tested the hypotheses that markers of small airway obstruction are associated with (i) increased asthma severity, (ii) impaired asthma control and quality of life and (iii) frequent exacerbations.

METHODS

Seventy-four adults with asthma and 18 healthy control subjects underwent impulse oscillometry (IOS), multiple breath inert gas washout (MBW), body plethysmography, single-breath determination of carbon monoxide uptake and spirometry. Patients completed the six-point Asthma Control Questionnaire (ACQ-6) and standardized Asthma Quality of Life Questionnaire [AQLQ(S)]. Asthma severity was classified according to the Global Initiative for Asthma (GINA) treatment steps.

RESULTS

The putative small airway obstruction markers Sacin , resistance at 5 Hz minus resistance at 20 Hz (R5-R20) and reactance area (AX) were not independently associated with asthma severity, control, quality of life or exacerbations. In contrast, markers of total (R5) and mean airway resistance of large and small airways (R20) were significantly higher in the severe asthma group compared with the mild-moderate group (0.47 vs. 0.37, P < 0.05 for R5; 0.39 vs. 0.31, P < 0.01 for R20). The strongest independent contributors to ACQ-6 score were R20 and forced expiratory volume in one second (% pred.), and the strongest independent contributors to AQLQ(S) score were R20 and forced vital capacity (% pred.). A history of one or more exacerbations within the previous year was independently associated with R20.

CONCLUSIONS AND CLINICAL RELEVANCE

Previously reported markers of small airway obstruction do not appear to be independently associated with asthma disease expression. In contrast, the IOS parameter R20, a marker of mean airway resistance of both large and small airways, appears to have independent clinical significance. These observations require confirmation in prospective longitudinal studies.

Synergy-COPD: a systems approach for understanding and managing chronic diseases

Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems. We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD. The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COPD.

Computational modeling of the obstructive lung diseases asthma and COPD

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling.

There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.

Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current state-of-the-art in techniques developed for pulmonary image analysis, development of structural models of the respiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models to investigate structure-function relationships in asthma and COPD.

Airway smooth muscle in asthma: linking contraction and mechanotransduction to disease pathogenesis and remodelling

Asthma is an obstructive airway disease, with a heterogeneous and multifactorial pathogenesis. Although generally considered to be a disease principally driven by chronic inflammation, it is becoming increasingly recognised that the immune component of the pathology poorly correlates with the clinical symptoms of asthma, thus highlighting a potentially central role for non-immune cells. In this context airway smooth muscle (ASM) may be a key player, as it comprises a significant proportion of the airway wall and is the ultimate effector of acute airway narrowing. Historically, the contribution of ASM to asthma pathogenesis has been contentious, yet emerging evidence suggests that ASM contractile activation imparts chronic effects that extend well beyond the temporary effects of bronchoconstriction. In this review article we describe the effects that ASM contraction, in combination with cellular mechanotransduction and novel contraction-inflammation synergies, contribute to asthma pathogenesis. Specific emphasis will be placed on the effects that ASM contraction exerts on the mechanical properties of the airway wall, as well as novel mechanisms by which ASM contraction may contribute to more established features of asthma such as airway wall remodelling.

Skin stem cell hypotheses and long term clone survival--explored using agent-based modelling

Epithelial renewal in skin is achieved by the constant turnover and differentiation of keratinocytes. Three popular hypotheses have been proposed to explain basal keratinocyte regeneration and epidermal homeostasis: 1) asymmetric division (stem-transit amplifying cell); 2) populational asymmetry (progenitor cell with stochastic fate); and 3) populational asymmetry with stem cells. In this study, we investigated lineage dynamics using these hypotheses with a 3D agent-based model of the epidermis. The model simulated the growth and maintenance of the epidermis over three years. The offspring of each proliferative cell was traced. While all lineages were preserved in asymmetric division, the vast majority were lost when assuming populational asymmetry. The third hypothesis provided the most reliable mechanism for self-renewal by preserving genetic heterogeneity in quiescent stem cells, and also inherent mechanisms for skin ageing and the accumulation of genetic mutation.