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Critical Reviews in Toxicology

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Computational modeling of lung deposition ofinhaled particles in chronic obstructive pulmonarydisease (COPD) patients: identification of gaps inknowledge and data

Koustav Ganguly, Ulrika Carlander, Estella DG Garessus, Markus Fridén, UlfG Eriksson, Ulrika Tehler & Gunnar Johanson

To cite this article: Koustav Ganguly, Ulrika Carlander, Estella DG Garessus, Markus Fridén,Ulf G Eriksson, Ulrika Tehler & Gunnar Johanson (2019) Computational modeling of lungdeposition of inhaled particles in chronic obstructive pulmonary disease (COPD) patients:identification of gaps in knowledge and data, Critical Reviews in Toxicology, 49:2, 160-173, DOI:10.1080/10408444.2019.1584153

To link to this article: https://doi.org/10.1080/10408444.2019.1584153

© 2019 The Author(s). Published by InformaUK Limited, trading as Taylor & FrancisGroup.

Published online: 23 Apr 2019.

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REVIEW ARTICLE

Computational modeling of lung deposition of inhaled particles in chronicobstructive pulmonary disease (COPD) patients: identification of gaps inknowledge and data

Koustav Gangulya, Ulrika Carlandera, Estella DG Garessusa, Markus Frid�enb,c, Ulf G Erikssond, Ulrika Tehlere andGunnar Johansona

aIntegrative Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; bRespiratory, Inflammation andAutoimmunity IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden; cTranslational PKPD, Department of Pharmaceutical Biosciences,Uppsala University, Uppsala, Sweden; dEarly Clinical Development, IMED Biotech Unit, Quantitative Clinical Pharmacology, AstraZeneca,Gothenburg, Sweden; ePharmaceutical Sciences, IMED Biotech Unit, Early Product Development, AstraZeneca, Gothenburg, Sweden

ABSTRACTComputational modeling together with experimental data are essential to assess the risk for particulatematter mediated lung toxicity and to predict the efficacy, safety and fate of aerosolized drug moleculesused in inhalation therapy. In silico models are widely used to understand the deposition, distribution,and clearance of inhaled particles and aerosols in the human lung. Exacerbations of chronic obstructivepulmonary disease (COPD) have been reported due to increased particulate matter related air pollutionepisodes. Considering the profound functional, anatomical and structural changes occurring in COPDlungs, the relevance of the existing in silico models for mimicking diseased lungs warrants reevaluation.Currently available computational modeling tools were developed for the healthy adult (male) lung.Here, we analyze the major alterations occurring in the airway structure, anatomy and pulmonary func-tion in the COPD lung, as compared to the healthy lung. We also scrutinize the various physiologicaland particle characteristics that influence particle deposition, distribution and clearance in the lung.The aim of this review is to evaluate the availability of the fundamental knowledge and data requiredfor modeling particle deposition in a COPD lung departing from the existing healthy lung models. Theextent to which COPD pathophysiology may affect aerosol deposition depends on the relative contri-bution of several factors such as altered lung structure and function, bronchoconstriction, emphysema,loss of elastic recoil, altered breathing pattern and altered liquid volumes that warrant considerationwhile developing physiologically relevant in silico models.

Abbreviations: CLE: centrilobular emphysema; COPD: chronic obstructive pulmonary disease; CFPD:computational fluid and particle dynamics; CSA: cross sectional area; CT: computed tomography; FEV1:forced expiratory volume in 1 second; FRC: functional residual capacity; FOT: forced oscillations singlefrequency sound waves; FVC: forced vital capacity; GOLD: global initiate for chronic obstructive lungdisease; HPLDB: hygroscopic particle lung deposition model B; HRCT: high resolution CT; IOS: impulsesof multiple frequency sound waves; LAA: Low attenuation area; Lm: mean linear intercept; MEF: max-imal expiratory flow; MPPD: multiple path particle dosimetry model; PET: positron emission tomog-raphy; PLE: panlobular emphysema; RV: reserve volume; SPECT: single photon emission computedtomography; TEF: tidal expiratory flow; TLC: total lung capacity; VC: vital capacity

ARTICLE HISTORYReceived 20 December 2018Revised 11 February 2019Accepted 14 February 2019

KEYWORDSEmphysema; chronicbronchitis; air pollution;inhalation; particle; COPD;in silico

Table of contents

Introduction ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 161COPD, chronic bronchitis, and emphysema ... ... ... ... ... 161Normal lung structure ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 162Factors influencing lung particle deposition ... ... ... ... ... 163Functional changes of COPD lungs ... ... ... ... ... ... ... ... ... 163

Pulmonary function ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...163Hyperinflation ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...163Breathing pattern ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...163Airway wall ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...164

Structural changes in COPD lungs... ... ... ... ... ... ... ... ... ... 164Alveolar parenchyma ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...164Small airways ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...164Vascularity ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...164Relationship between airway obstruction and

emphysema ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...164In silico lung models ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 165Modeling COPD lungs with particle lung depos-

ition models ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 166Lung structure ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...167

CONTACT Koustav Ganguly koustav.ganguly@ki.se Unit of Work Environment Toxicology, Institute of Environmental Medicine, Karolinska Institutet; Box287, SE-171 77 Stockholm, Sweden� 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

CRITICAL REVIEWS IN TOXICOLOGY2019, VOL. 49, NO. 2, 160–173https://doi.org/10.1080/10408444.2019.1584153

Bronchoconstriction ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...167Emphysema... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...168Elastic recoil ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...168Breathing pattern ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...169

Physiologically relevant modeling of the COPD lung ... 169Perspective ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 169Acknowledgements ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 170Declaration of interest ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 170

Introduction

Chronic obstructive pulmonary disease (COPD) accounts formore than 3 million deaths every year (http://www.who.int/mediacentre/factsheets/fs315/en/). COPD is the 3rd leadingcause of death globally and has enormous impact on thesociety, patients and their families in terms of cost and qual-ity of life (Ferkol and Schraufnagel 2014; https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death).Tobacco smoking is the main cause for the development ofCOPD, but non-tobacco related causes like biomass smokeexposure, air pollution, exposure to occupational dusts andchemicals, genetic pre-disposition, impaired lung develop-ment and accelerated aging also predispose individuals toCOPD (Vogelmeier et al. 2017). COPD is diagnosed based onpersistent airflow obstruction measured by spirometry.However, spirometry measurements reflect the sum of all thedifferent complex and heterogenous COPD pathologies.Agusti (2014) explained the “complexity of COPD” as thenon-linear dynamic interaction of intra-pulmonary and extra-pulmonary components with time. “Heterogeneity” of COPDhas been explained as the fact that not all the complexitiesare present among all COPD patients at any given point oftime (Agusti 2014). Thus, in terms of causality and patho-physiology, COPD represents an extremely heteroge-neous condition.

Exposure to particulate matter from sources such asindoor (e.g. biomass smoke) and outdoor air pollution cannot only initiate and promote the development of COPD butcan also trigger exacerbation of respiratory symptoms amongCOPD patients (Dockery et al. 1993; Pope 2000; Samet et al.2000). In order to better understand the pathophysiology ofCOPD development and COPD exacerbations, a good under-standing of particle deposition is warranted. In addition,knowledge of particle deposition in diseased lungs is usefulfor optimization of therapeutic inhalation strategies thatinvolve delivery and targeted deposition of aerosolized drugto the diseased part of the lung (Schulz 1998; B€ackman et al.2018). By tuning particle properties and breathing conditions,distribution to different parts of the lung can be adjusted.Information on particle deposition can be generated fromcomputational particle deposition models. Different types ofparticle deposition models are available (Hofmann 2011).However, most, if not all, models are built on data fromhealthy adult (male) lungs (ICRP 1994). Moreover, most mod-els are based on ideal aerosols (spherical particles), which isfar from the real scenario.

Direct experimental measurements in humans are mostlylimited to total particle deposition in the lungs and occasion-ally to rough regional deposition. In contrast, anatomicallyand physiologically based computational models can be usedto predict and examine more detailed and site-specific infor-mation on the deposition pattern. Such in silico models arethus a good complement to experimental measurements.An additional advantage with in silico models is that they,with adequate understanding of the (patho)-physiology, canbe extended to a wider population (including subgroupssuch as COPD patients) where the feasibility to performexperimental studies are smaller.

In this review, we scrutinize the data required to modelparticle deposition in the COPD lung in three steps. First, weprovide an overview of COPD pathogenesis followed by adescription of models available for the prediction of particledeposition in the COPD lung. Finally, we discuss the datarequired to improve the prediction of particle deposition anddistribution in the COPD lung.

COPD, chronic bronchitis, and emphysema

COPD is diagnosed as a persistent airflow obstruction deter-mined by the ratio of post-bronchodilator forced expiratoryvolume in 1 s/forced expiratory volume (FEV1/FVC) of <70%(http://goldcopd.org/; Vogelmeier et al. 2017). The severitystaging (stages I-IV) of COPD is based on the percentagedecrease of FEV1 from predicted values (Figure 1; http://gold-copd.org/; Vogelmeier et al. 2017). Diagnosis of COPD is diffi-cult during the initial stages of the disease due to the lack ofcorresponding reflection in pulmonary function tests(Niewoehner et al. 1974; Brusasco and Martinez 2014).However, as a result of the profound anatomical and struc-tural alterations occurring in the lung already during earlystages of COPD, it is likely that the particle deposition anddistribution in the different regions of lung may also beaffected. Thus, a severity stage based model of the COPDlung would provide deeper understanding of particle depos-ition and distribution along with the disease progression.

Chronic bronchitis and emphysema are the two commonCOPD associated phenotypes (http://www.who.int/respira-tory/copd/en/). Although chronic bronchitis and emphysemaare not included in the definition of COPD per se (Vogelmeieret al. 2017), their characterization is essential for understand-ing the disease pathogenesis and defining the therapeuticstrategies (Brusasco and Martinez 2014). In chronic bronchitis,obstruction of small airways, inflammation, mucus glandenlargement, excess mucus production, and goblet cellhyperplasia (Saetta et al. 2000; Hogg et al. 2004; Willemseet al. 2004) is accompanied with continuous cough(> 3months duration per year) (Fabbri et al. 2003, 2004;Vestbo et al. 2013). Emphysema on the other hand involvesenlargement of lower airspaces, destruction of lung paren-chyma, loss of lung elasticity, and closure of small airways(Macnee 2005; Timmins et al. 2011). Therefore, in order tomodel particle deposition in the COPD lung, consideration ofchronic bronchitis and emphysema is essential.

CRITICAL REVIEWS IN TOXICOLOGY 161

Normal lung structure

Lungs are composed of about half a liter of tissue, similarvolume of blood and �4.3 liters (l) of air in a healthy“standard” man (30 y, 1.75m, 70 kg) consisting of the trachea,two main bronchi, bronchioles, alveolar ducts and alveolarsacs. (Murray 2010). The classical Weibel’s model (Weibel1963) described 23 generations of branching airways in thehuman respiratory tract that is classified into two zones: (a)Conducting zone (generations 0–16) and (b) Respiratory zone(generations 17–23) (ICRP 1994). The Conducting zone(100–150ml) comprises of the trachea, bronchi, bronchiolesand terminal bronchioles and delivers air to the respiratoryzone (West 2007; Wang et al. 2014). It has a thick airway walland is devoid of any alveoli, thereby not participating in theprocess of gas exchange (Weibel 1963). The respiratory zone(2.5–3 l) comprises of the respiratory bronchioles, alveolarducts and alveolar sacs and facilitates the process of gasexchange at the blood-air barrier. Alveolar ducts and alveolarsacs are covered by alveoli, the gas exchange units of thelung (alveolar surface area: 70–80m2) (Weibel 1963; West2007; Wang et al. 2014). Mucociliary clearance is the process

of mucus transport towards the throat by the coordinated cil-iary beating (20mm/min in trachea to 1mm/min in smallperipheral airways) and expiratory airflow to clear foreignobjects out of the lung (West 1992; Wang et al. 2014). Thisprocess is an essential mechanism to clear respirable particu-late matter from the respiratory tract. On the other hand,macrophagic phagocytosis is the main particle clearancemechanism within alveoli. Adult human lung consists ofabout 500 million alveoli with about 12–14 resident macro-phages in each alveoli. Resident alveolar macrophages con-tribute to approximately 1% of the total alveolar surface area(Patton and Byron 2007; Geiser 2010; Geiser and Kreyling2010; Wang et al. 2014). Particles that are small enough topenetrate into the alveolar tissue may also be cleared viatranslocation to lung-associated lymph nodes as shown inseveral animal species including humans (Snipes et al. 1983;Kitamura et al. 2007; Choi et al. 2010; Nakane 2012). Thetranslocation appear to be small (<0.1% of the depositeddose) but can increase due to disease and inflammation inthe lung (Nakane 2012; Keller et al. 2014; Bevan et al. 2018).Translocation of various types of fine and ultrafine particles

Figure 1. An integrated view on the various factors for consideration to model a chronic obstructive pulmonary disease (COPD) lung in a physiologically relevantmanner for particle deposition, distribution and clearance studies. % FEV1 pred.: percent of predicted forced expiratory volume in 1 second; FVC: forced expira-tory volume.

162 K. GANGULY ET AL.

have also been detected in the mediastinal and/or hilarlymph nodes in mice and rats, following inhalation, intra-nasal and/or intra-tracheal instillation (Shwe et al. 2005; vanRavenzwaay et al 2009; Pauluhn 2012; Nakane 2012). Anotherclearance mechanism, of importance particularly for solubleparticles and small nanoparticles, is absorption into the sys-temic circulation (Kermanizadeh et al. 2015).

Factors influencing lung particle deposition

A certain fraction of inhaled particles is deposited in therespiratory system following contact with the lining fluid ofthe airways (Edsb€acker et al. 2008). Particle deposition isinfluenced by several factors related to the particle propertiesas well as physiological and anatomical features of the sub-ject inhaling those particles (Schulz 1998; Schulz and Muhle2000). The main physical processes determining pulmonaryparticle deposition are (i) impaction, (ii) sedimentation and(iii) diffusion (Tena and Clar�a 2012). The extent and patternof particle deposition are driven by: (i) particle size, shapeand density, (ii) physicochemical properties of the inhaledaerosol, (iii) airflow velocity, (iv) breathing patterns, (v) lunggeometry (e.g. airway diameter and number of alveoli) andstructure, (vi) anatomy of the nasal, oral and pharyngealareas, and (vii) temperature and humidity (Tena and Clar�a2012; Jinxiang et al. 2014; Borghardt et al. 2015). COPD asso-ciated changes in the airway dimensions (bronchoconstrictionand hyperinflation), lung structure (emphysema) (Wagner2003), altered airflows and breathing patterns (L€oring et al.2009) and reduced lung elasticity (Wagner 2003) may affectparticle deposition. Thus, modeling of a COPD lung warrantsconsideration of the associated structural, dimensional andfunctional changes.

Functional changes of COPD lungs

Pulmonary function

Spirometry measurement to demonstrate irreversible airflowobstruction (FEV1/FVC< 0.70) is essential for the clinical diag-nosis of COPD (Rabe et al. 2007; Vogelmeier et al. 2017).However, a decreased FEV1/FVC does not always indicate air-flow obstruction particularly when the FEV1 and FVC valuesare within or above the normal range (Brusasco and Martinez2014). Moreover, in the early stages of COPD, FEV1 and air-way conductance are not affected as the structural changesof lung are mainly localized in the small airways (Brusascoand Martinez 2014). However, changes in lung functionparameters other than FEV1 may as well impact the diseaseseverity. For example, in the case of obstructive disorders,the maximal expiratory flow (MEF) is limited at lower valuesthan normal. During early stages of COPD, MEF remainsmuch larger than tidal expiratory flows (TEF) resulting inavailability of a sufficient “flow reserve” for increasing minuteventilation at rest or normal daily activities. At later stages ofthe disease when airflow limitation is increased, the “flowreserve” is decreased (Eltayara et al. 1996; Brusasco andMartinez 2014). Thus, apart from FEV1 and FVC values, MEF

and TEF may also serve as important data sets for model-ing purposes.

Hyperinflation

Lung hyperinflation is a major functional consequence ofaltered lung mechanics in COPD (Pride and Peter 2011;Brusasco and Martinez 2014). It affects total lung capacity(TLC), residual volume (RV) and functional residual capacity(FRC). Increased RV in COPD occurs due to reduced lung elas-tic recoil or airway narrowing or combination of both (Bateset al. 1962, 1966; Brusasco and Martinez 2014). Increased TLCin COPD patients have been reported in emphysematouslungs (Berend et al. 1980; Loyd et al. 1966; Naunheim et al.2006; Simon et al. 1973; Brusasco and Martinez 2014).Changes in RV and TLC determine changes in vital capacity(VC) and also in FEV1. VC may be normal or increased duringearly phases of emphysema because the increase in RV iscompensated by an increase in the TLC. With advancedstages of the disease, the RV increases more than the TLC,thus reducing the VC and also contributing to the FEV1decrease (Brusasco and Martinez 2014). Increased FRC inCOPD is attributed to the COPD-associated emphysema andhyperinflation (Akimoto 2003; Halbert et al. 2006). FRCincrease in COPD may be due to static or dynamic or bothmechanisms (Sharp 1968; Brusasco and Martinez 2014). FRCvalues in COPD patients have been shown to vary from 2.1 lto 12.5 l (Jamaati et al. 2013). Other studies reported averageFRC values in COPD patients to be 7.2 l and that of healthyindividuals to be 3.7 l (Gorman et al. 2002). Lung dimensionsand lung geometry are affected not only by COPD severitybut also by age and gender (US-EPA 2004). Thus, it is import-ant to correct for these factors in order to predict the effectof COPD on the particle deposition.

Breathing pattern

Another characteristic observation among COPD patients isshallow and altered breathing pattern. The mechanism behindthe altered breathing pattern is not completely understood,but weakness of the respiratory muscles has been suggestedas one of the main factors. Comparison of breathing patternrevealed decreased inspiratory time and reduced expiratoryairflow among COPD patients compared to healthy individuals(L€oring 2009; Wilkens et al. 2010; Vestbo et al. 2013;Motamedi-Fakhr et al. 2016). However, effects on other breath-ing parameters like tidal breathing, exhalation time andbreathing frequency were more heterogeneous among COPDpatients (L€oring 2009; Wilkens et al. 2010; Yamauchi et al.2012; Motamedi-Fakhr et al. 2016). However, measuring techni-ques (eg. use of nose-clips, mouth pieces or masks) may sig-nificantly influence the breathing pattern by increasing thebreathing frequency (Askanazi et al. 1980).

Airway wall

The physical properties of the airway wall layer (mucosa, sub-mucosa and adventitia) can influence the airflow (Brusasco

CRITICAL REVIEWS IN TOXICOLOGY 163

and Martinez 2014). Computational modeling studies suggestthat the airway smooth muscle tone in the cartilaginous air-ways is associated with airflow obstruction in COPD (Lambertet al. 1993; Brusasco and Martinez 2014). Thickening of themucosal layer of the airway wall is regarded as the primarymechanism for increased airflow resistance and airway clos-ure (Moreno et al. 1986; Wiggs et al. 1992). Presence ofmucus within the airway lumen (Yager et al. 1989) andincreased surface tension due to altered surfactant integrity(Enhorning et al. 1995) also contributes to the airway narrow-ing. Furthermore, loss of lung elastic recoil in emphysema(increased pulmonary compliance) contributes to reducedexpiratory airflow (Brusasco and Martinez 2014).

Structural changes in COPD lungs

The airflow obstruction in COPD patients is primarily due tochanges in the peripheral conducting airways (chronic bron-chitis) (Hogg et al. 1968) and destructive changes in bron-chioles, alveolar ducts and alveoli (emphysema) (Thurlbeck1976; Brusasco and Martinez 2014).

Alveolar parenchyma

Destruction of the alveolar parenchyma is the main featureof an emphysematous lung. Centrilobular emphysema (CLE)is also associated with thickening of small airways and gener-ally affects upper lung regions. On the other hand, panlobu-lar emphysema (PLE) exhibits uniform dilatation anddestruction of the entire secondary lobule (Thurlbeck 1963).Emphysematous destruction of the lung affects the expiratoryflow rate due to loss of lung elastic recoil and destruction ofalveolar attachments to peripheral airway walls (Nagai et al.1995; Brusasco and Martinez 2014).

Small airways

Airways with a diameter below 2mm are the main sites ofairway narrowing in COPD (Hogg et al. 1968; Hogg 2012). Afour to forty-fold increase in the small airway resistanceamong COPD patients compared to healthy lung have beenreported (McDonough et al. 2011; Hogg et al. 2013). Airwayssmaller than 2mm in diameter account for �20% of the totallower airway resistance and are also the major site of airwayobstruction in COPD (Hogg 2012; Hogg et al. 2013). Loss andnarrowing of small conductive airways precede the develop-ment of emphysema which in turn explains the increase inresistance among COPD patients. Long term smokers exhibita higher degree of inflammation and more bronchioles(diameter< 0.4mm) compared to nonsmokers. The numberof airways with diameter< 2.0mm are however similaramong long term smokers without COPD and nonsmokers(Cosio et al. 1980). In clinically established COPD subjects, areduced number of airways with diameter 2.5–2.0mm aredetected (Matsuba and Thurlbeck 1972; McDonough et al.2011). Reduced number of terminal bronchioles in patientswith mild COPD prior to the occurrence of CLE have beenalso reported through micro-computed tomography (CT) based

stereological studies (McDonough et al. 2011). Thickening ofairway wall also contributes to airway obstruction (Landseret al. 1982; Moreno et al. 1986; Wiggs et al. 1992).

Vascularity

Reduced vascularity within alveolar septa in emphysema dueto endothelial dysfunction is a well-recognized phenomenon(Liebow 1959; Brusasco and Martinez 2014). However, thecorresponding impact of vascular changes on lung mechanicsand pathology is poorly understood. Quantitative CT meas-urements of the total cross-sectional area (CSA) of small pul-monary blood vessels (<5mm2) at sub-segmental levelsstrongly correlate with the extent of emphysema (Matsuokaet al. 2010). In the study by Matsuoka et al. (2010), a strongnegative correlation (r¼�0.83; p< 0.0001) between % CSAof pulmonary vessels (<5mm2) and % low attenuation area(LAA, less than 950 Hounsfield units is defined as emphy-sema) have been reported. Percent CSA of pulmonary vessels(5–10mm2) was weakly (r¼�0.25; p¼ 0.0004) correlated toemphysematous change (Matsuoka et al. 2010). Incorporationof vascular changes in COPD modeling studies may beimportant, as reduced vascularity may impair particle clear-ance. However, quantitative data on the vasculature ofhealthy and diseased lung are lacking.

Relationship between airway obstructionand emphysema

Very few studies have addressed the relationship betweensmall airway obstruction and emphysematous destruction ofthe lung. McDonough and colleagues used multi-detectorcomputed tomography (micro-CT) to compare the number ofairways (2.0–2.5mm) among 78 patients with various stagesof COPD (with CLE and PLE) judged according to the GOLDguidelines. Micro-CT was used to measure the mean linearintercept (Lm, i.e. the distance between alveolar walls), ter-minal bronchioles per milliliter (ml) of lung volume, alongwith the minimum diameters and CSA of the terminal bron-chioles. The morphometric data were represented as: (i) num-ber of small airways (2.0–2. 5mm diameter) per pair of lungsin COPD patients of GOLD stages 0–4; (ii) number of airways(2.0–4.0mm) per generation per pair of lungs in control, cen-tri-and pan-lobular emphysema to reconstruct the bronchialtree; (iii) Lm of alveoli in CLE and PLE subjects and their fre-quency distribution; iv) number of terminal bronchioles perml of lung volume in CLE and PLE patients; and (v) small air-way profile square centimeter (cm2) in various regions of dis-eased lung (based on Lm) and corresponding airway wallthickness. The study further included data on age, sex,weight, height, pack-years of smoking, FEV1 (% predicted),FEV1/FVC, total lung volume (% of predicted value and vol-ume), lung mass, number of terminal bronchioles (number/ml of lung volume and total number), CSA of terminal bron-chioles (mm2; average and total) and minimum luminal diam-eter (mm). The findings of McDonough et al. (2011), suggest a4–40 fold increase in peripheral resistance in COPD lungs.The corresponding total number of terminal bronchioles in

164 K. GANGULY ET AL.

CLE and PLE patients was 2400± 600 per ml and 6200 ± 2100per ml, respectively, compared to 22,300 ± 3900 per ml incontrol subjects.

From the above reviewed information it is apparent thatsmall airways (<2mm in diameter) are the major site ofCOPD pathology and that they are involved during the earlycourse of the disease when spirometry or imaging tools donot provide adequate information for clinical diagnosis(McNulty and Usmani 2014). However, studies on small air-ways in COPD are limited mainly due to the small size andinaccessibility for biopsies (McNulty and Usmani 2014).Impulse oscillometry is a more sensitive technique in detect-ing small airway obstruction compared to spirometry. Forcedoscillations of single frequency sound waves (FOT) orimpulses of multiple frequency sound waves (IOS) are pushedinto the lungs as pressure waves to measure respiratoryresistance (frequency independent under healthy conditionbut frequency dependent under airway obstruction) andreactance (elastic and inertial lung properties; frequency inde-pendent). Importantly, impulse oscillometry is easy to useand effort independent technique therefore suitable for chil-dren and patients with severe lung diseases who cannot effi-ciently perform forced maneuvers required for spirometry(McNulty and Usmani 2014; Salvi 2015). Thus, assessment ofpulmonary mechanics (reactance) through FOT/IOS may pro-vide deeper insights of airflow obstruction in COPD. Recent

developments of imaging technologies like high resolutioncomputed tomography (HRCT), hyperpolarized magnetic res-onance imaging, scintigraphy, single photon emission com-puted tomography (SPECT), positron emission tomography(PET) that may provide resolution capabilities beyond com-puter tomography to visualize airways as small as 2.0mm todescribe quantitative changes in the small airways, drugdeposition, inflammation, and ventilation-perfusion relation-ships can be useful (McNulty and Usmani 2014). Table 1 sum-marizes the functional and structural parameters that warrantconsideration for physiologically relevant in silico modeling ofparticle deposition in the COPD lung.

In silico lung models

Different types of in silico models have been successfullyused to predict deposition of particles in healthy humanlungs (Hofmann 2011; Isaacs et al. 2012; Longest andHolbrook 2012; Darquenne et al. 2016). Currently, availablelung models can be grouped into: (a) site-specific lung mod-els and (b) whole lung models depending on the applicationand region of interest in the respiratory system.

Site-specific models typically provide information aboutlocal deposition and three-dimensional localization within tar-geted sites of the lung such as bronchial bifurcations, larynx,nose, mouth, and throat (Longest and Holbrook 2012;

Table 1. Summary of functional and structural parameters of the normal and chronic obstructive pulmonary disease (COPD) lung that warrant consideration forincorporation in the in silico particle deposition models.

Functional andstructural parameters Healthy COPD

Comments for in silicomodeling purposes

FEV1/FVC >0.70 <0.70 Based on FEV1, models can be used forseverity staging

FRC 3.7 l 2.14–12.5 l 3300ml is used in most modelsElastic recoil Not included in models Loss of elastic recoil increases with

COPD severityCompliance is the corresponding lung

function parameter; not evidentlypossible to accommodate in currentmodeling frameworks.

Emphysema Not applicable CLE: centrilobular emphysemaPLE: panlobular emphysema

Reducing respiratory airway generations(17–23) and increasing alveolar vol-ume to mimic hyperinflation

Breathing pattern Inhalation time: 2.5 sExhalation time: 2.5 s

Inhalation time: 1.0 sExhalation time: 4.5 sShallow, prolonged or shortened ornot affected

Increasing inspiratory airflow and reduc-ing expiratory airflow

Bronchoconstriction Not applicable Airway diameter reduction:Generations 3–9: 6–90%Upper airway generations may bereduced; data <2mm diameter air-ways not available

Gradient reduction of airway diameterto mimic severity levels

Inflammometry Not applicable Increased inflammation with recruitmentof inflammatory cells (eg. PMNs,macrophages)Very limited quantitativedata available

Modeling approaches not available

Vascularity 50–100 m2 in the alveolar surface Reduction of small pulmonary vessels(<5mm2)Very limited data available

Modeling approaches limited

Terminal bronchioles 22300 ± 3900 CLE: 2400 ± 600PLE: 6200 ± 2100Very limited data available

Modeling approaches not available

Mucociliary beating 20mm/min (trachea)1mm/min (small peripheral airways)

Slower but precise data not available Modeling approaches not available

Macrophagic clearance 500 million alveoli12–14 resident macrophages occupy-ing 1% of total alveolar surface area

Macrophage recruitment; Very limitedquantitative data available

Modeling approaches not available

FEV1: forced expiratory volume in 1 s; FRC: functional residual capacity.

CRITICAL REVIEWS IN TOXICOLOGY 165

Kolanjiyil et al 2017). These types of models are useful in thedesign of dry powder inhalers and also to identify areas withhigh local concentrations, the so-called “hot spots” in bifurca-tions. Site-specific lung models address particle transport anddeposition by computational fluid and particle dynamics(CFPD) where concepts of complex airway geometry andflow physics are utilized. CFPD modeling is typically advancedand computationally intensive, requiring trained interdiscip-linary scientists and high-performance hardware withadequate processing and extensive capacity. Site-specific clin-ical data are difficult to obtain and have restrained the valid-ation of models.

The currently available whole lung models mainly differ intwo aspects: (i) lung morphometry and (ii) computationaltechnique (Hofmann 2011; Ruzer and Harley 2013). Thechoice of lung structure can range from simplified regionalcompartment models to detailed bronchial and acinar airwaystructures. Among the simplified lung structures, the resem-blance with real human lungs varies considerably. Somedescribe the lung as regional compartments (Koblinger andHofmann 1985) whereas others define the entire lung eitheras a single trumpet-shaped entity with a summed airwaycross-sectional area that increases with the distance from themouth (“trumpet” models) (Taulbee and Yu 1975), or as acontinuum of airway branches that branch symmetrically (sin-gle path models) (Yeh and Schum 1980) or asymmetrically(deterministic or stochastic multiple path models) (Yeh andSchum 1980; Asgharian et al. 2001) into daughter branches.

The applied computational techniques used in the wholelung models are thus either empirical, deterministic or sto-chastic. A major advantage of the empirical and semi-empir-ical models is that deposition is calculated by fittingalgebraic relationships to experimental human data. Thesemodels are also simple to use and do not require sophisti-cated computer programs. However, the experimental datasets on deposition have low resolution as they are limited toregions, making extrapolation to new scenarios, such asCOPD, unreliable.

Deterministic modeling techniques use simplified assump-tions about airway geometries and airflow conditions toderive analytical solutions of air and particle motion. Themodel tracks the path of a population of particles within thelung tree (Eulerian) utilizing analytical solutions based onmechanistic understanding of physiological and physicalmechanisms. The models can be used on personal computersusing freely available and user-friendly dedicated software.

In the stochastic approach, the morphology of the lung(airway diameter, airway length, and bifurcation angles) isconsidered to vary in a random manner within predefinedlimits. The particle path down the lung tree is tracked by fol-lowing either a single particle (Lagrangian approach) or aparticle population (Eulerian). A major advantage of the sto-chastic models is that they allow simulation of biological vari-ability within the lungs of an individual as well as thevariability between subjects. The model complexity requirestrained user and access to specialized programs.

Two widely used freely available models describing lungdeposition in a healthy person are: the Multiple-Path ParticleDosimetry Model (MPPD) (ARA 2017), and the Hygroscopic

Particle Lung Deposition model B (HPLDB) (Ferron et al.1988). Both models are based on a symmetrical lung struc-ture and a deterministic approach. The MPPD model uses theYeh and Schum (1980) model as default, whereas the HPLDBmodel uses Weibel’s (1963) symmetrical lung as default. TheWeibel (1963) model describes the lung as a continuum of(airway) ducts, each of which branch into twosmaller airways.

Modeling COPD lungs with particle lungdeposition models

The heterogenicity of COPD combined with the lack of mor-phological data makes lung modeling of particle depositionchallenging. Several experimental studies on particle depos-ition in COPD patients have been carried out; however, wefound only four clinical studies where COPD patients versushealthy individuals were compared. The results from thesefour studies were inconclusive and the patient groups weresmall (n¼ 4–23 patients) (de Backer et al. 2010; Fazzi et al.2009; Scheuch et al. 2009; H€ausserman et al. 2007 ). In some,the total deposition was not significantly (p> 0.05) affected,whereas in others increased peripheral deposition in COPDwas observed. It is plausible that the altered deposition pat-tern of aerosol reported in case of COPD lungs is diseaseseverity driven. The extent to which COPD pathophysiologymay affect aerosol deposition depends on the relative contri-bution of several factors such as altered lung structure andfunction, bronchoconstriction, emphysema, loss of elasticrecoil, and altered breathing pattern. These COPD relatedphysiological factors require integration into in silico lungmodels by modification of model parameters, equations, andstructures to address the complexity and heterogeneity of aCOPD lung.

As end users, researchers are limited to tune most of thepredefined parameters in the freely available in silico lungmodels discussed above (HPLD and MPPD). These predefinedparameters do not include for example airway diameters andalveolar volume. Tunable parameters are limited to changesin particle properties, tidal volume, FRC, breathing patternand to symmetric or asymmetric lung structure. This signifi-cantly limits the usefulness of these in silico lung modelswhen moving from the healthy to the COPD lung.

On the other hand, other investigators have integrateddisease related physiological factors into their models tostudy the effect of COPD on particle deposition. These mod-eling efforts address bronchoconstriction, emphysema orboth (Tables 2 and 3). In some, alterations in lung structure,breathing pattern and elastic recoil have also been incorpo-rated. Local deposition in the diseased lung has typicallybeen studied using CFPD (Zhang et al. 2018; Chen et al.2012; Luo et al. 2007) whereas modeling efforts of particledeposition in the whole lung has usually used stochastic ordeterministic approaches (Sturm and Hofmann 2004;Svartengren et al. 2004; Segal et al. 2008; Sturm 2013).Table 2 summarizes the characteristics of the common usedwhole lung particle deposition models.

166 K. GANGULY ET AL.

Lung structure

The CT image based lung deposition models make use of themost realistic lung structures. These three-dimensional mor-phological models typically simulate deposition by CFPD. Sofar, such CFPD models have been restricted to local parts ofthe lung due to computational restrictions (Burrowes 2014;Nowak et al. 2003). Asymmetrical lung structures are closerto reality than symmetrical ones. It has been shown thatasymmetry in branching results in a high variability in depos-ition within airways of the same generation (Hofmann et al.2000). Nevertheless, similar regional and generation-by-gener-ation deposition predictions have been obtained with sym-metrical and asymmetrical models (Asgharian et al. 2001).Further, asymmetrical as well as symmetrical models accur-ately describe total particle deposition in vivo (Anjilvel andAsgharian 1995) and in humans (Segal et al. 2000). SinceWeibel�s airway volumes (scaled to a FRC of 3300ml) per gen-eration is similar to CT-based airway volume measurements(Fleming et al. 2004), the choice of particle deposition mod-els based on a symmetrical lung structure as described byWeibel (1963) has been suggested to be adequate for model-ing purposes. It has been previously demonstrated that thechoice of lung structure, in particular, airway dimensions andnumber of airway generations (Yu and Diu 1982), and thevolume of the dead space (Rissler et al. 2017) significantlyinfluence the prediction of particle deposition.

Bronchoconstriction

Bronchoconstriction results in narrowing of the airways dueto shrinkage of the diameter, locally or throughout the lungbranch generations. This heterogeneity changes the airflowdynamics in the lung. In modeling efforts of COPD condi-tions, correlations between severity of COPD and reduced air-way diameters and numbers of airways were identified.Reduction of airway diameters with up to 40% and numberof airways with up to 80% were observed. (Kurashima et al.2013; Williamson et al. 2011; McDonough et al. 2011). Due tolimitations of CT resolution, data on airway dimensionsbeyond generation 6 (<2.0mm) is difficult to obtain. As aresult of airway narrowing, the aerodynamics of airflow andthe airway resistance are altered. Airway resistance is mainlyaffected by changes in airway diameter in the conductingzone (generations 0–16). Airflow deceases with generationnumber, starting as turbulent with plug flow profile and endsas non-turbulent with parabolic flow characteristics.Narrowing of airways becomes critical when bronchioles areblocked resulting into cutting off the distal airways. Thehigher up in the branching airway blockage takes place, themore the distal airways are affected. Reduction of airwaydiameter locally is a feasible approach to mimic bronchocon-striction. This might require changes in the model to addressaerodynamic alterations compatible with large and local nar-rowing of sections of the lung i.e. border effects (Szoke et al.2007; Segal et al. 2008; Strum 2013; Svartengren et al. 2004;Sturm and Hofmann 2004; Farkhadnia et al. 2016; Sturm2017). However, freely available lung models do not offer thepossibility to include bronchoconstriction. Modeling ofTa

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CRITICAL REVIEWS IN TOXICOLOGY 167

bronchoconstriction by reducing airway diameters in the con-ducting airways have been demonstrated to result in higherdeposition in the alveolar region (Svartengren et al. 2004;Sturm 2013; Segal et al. 2008). Deposition in the bronchialregion varies and depends on the simulation conditions suchas breathing pattern, particle properties, and lung dimen-sions. Assumption of 25 to 75% open airways from gener-ation 9 and downwards resulted in an increased particledeposition fraction in the alveolar region when a slow inhal-ation flow rate was used (0.05 l/s), but similar total lungdeposition was observed for healthy and diseased patients(Svartengren et al. 2004).

Emphysema

Lung hyperinflation, due to destruction of the alveolar paren-chyma, in emphysema results in increased alveolar volumeand fewer alveoli. In the freely available tools, effects ofemphysema cannot be simulated. In other models, differentapproaches have been used to integrate emphysema intowhole lung models. The common theme is to increase vol-ume of the alveoli. In the deterministic symmetric model,alveolar degeneration in COPD patients was described byincreasing the alveolar volume by 10 to 30% (Segal et al.2008). No major effect compared with healthy lung wasobserved. In the stochastic asymmetrical model applied bySturm and coworkers, four different types of emphysemawere simulated; (1) centriacinar, (2) paraseptal, (3) panacinar(4) bullous. The differences between these four types are

related to the alveoli structure. Alveoli are connected to theconducting airways from generation 12 to 21 and after thaton the alveolar duct. The volume of the alveoli increases anddistributes further down along the airway generation whenthe emphysema goes from centriacinar to bullous. For allfour types of emphysema, the calculated fraction of particlesdeposited in the entire lung as well as in the alveolar regiondecreased compared to the healthy lung (Sturm andHofmann 2004; Sturm 2017).

Elastic recoil

During breathing the lung rhythmically expands and con-tracts. Since bronchioles are relatively stiff, the major parts ofthese movements take place in the alveolar region.Expansion and contraction of the alveoli have been includedin CFPD simulations of the healthy lung (Kolanjiyil et al.2017). So far, the loss of elastic recoil (increased compliance)has not been included in whole lung particle depos-ition models.

Breathing pattern

The existing models do not adequately address the dynamicsof breathing i.e. how the lung is filled with air, an especiallyimportant for COPD patients compared with healthy subjects.The breathing pattern, and in particular the inhalation timehave been shown to influence the deposition of particles inthe lung (Falk et al. 1999; Rissler et al. 2017; Jakobsson et al.

Table 3. The various lung structural and functional properties relevant to chronic obstructive pulmonary disease (COPD) that have been addressed in the existingin silico models.

Whole lung particle deposition models addressing COPD features

Reference Segal 2002 Sturm and Hofmann 2004 Sturm 2013 Sturm 2017 Svartengren et al. 2004Modeling approach Deterministic Stochastic Stochastic Stochastic DeterministicParticle tracking

approachEularian Lagarian Lagarian Lagarian Eularian

Lung structure(simplified)

Symmetric Asymmetric Asymmetric Asymmetric Symmetric

Experimental data Airway resistance, FRC No No No FEV1%, Airway resistanceReported particle

deposition inthe lung

0–23 airwaygenerations

ALV ET, TUB, ALV Total lung BB, bb, ALV

Modeling addressesBronchoconstriction Yes Yes Yes No YesEmphysema Yes Yes No Yes NoElastic recoil No No No No NoBreathing conditions No No Yes No YesLung clearance No No Yes No YesMucus clearance No No Yes No No

Modeling parameter valuesParticle size 1 mm 1nm–10 mm 10 nm–10 mm 0.84 mm 6 mmParticle density 0.91 g/cm3 � � 1 g/cm3 2.13Inspiratory flow (l/s) 0.5 0.25–0.5 � 0.25 0.05Inhalation time (s) 1 2 � 4 20Duty cycle (Ti/Ttot) 0.5 0.5 � 0.5 �Tidal volume (TV, ml) 500 500–1000 � 1000 1000Functional residualcapacity (FRC, ml)

1850–6820 3300–5000 3300 3300 3400–5400

Other comments Compared parabolicand plug flow.

Compared differentmixing factorsin alveolar sac

Addresses clearanceand compared sittingto light-work breath-ing conditions

Injection of bolusdoses at differenttime points dur-ing inhalation

Slow inspirationflow for tar-get deposition

ALV: alveolar region, BB: bronchial region, bb: bronchiolar region, ET: extra-thoracic region, FEV1%: percent predicted forced expiratory volume in 1 s, FRC: func-tional residual capacity, Raw: total airway resistance, TUB: tubular compartment containing the entire bronchial network, �: not reported.

168 K. GANGULY ET AL.

2016). To some extent, alteration of breathing pattern can beaddressed in the existing models, including the freely avail-able models, by changing the relevant parameters, namelyinhalation time, exhalation time and/or breath holding time.The common approach in the modeling of particle depos-ition in COPD patients is to set inhalation and exhalationtimes equal with no breath holding (Sturm and Hofmann2004; Sturm 2013, 2017 and Segal et al. 2008). However,there seems to be substantial disagreement and contradict-ory data regarding the effect of COPD on the modelingparameters related to airflows and breathing pattern. Clinicalmeasurements on COPD patients indicate that these param-eter values can vary. Overall, it appears that the inhalationtime is shorter than the exhalation time and that the differ-ence increases with disease severity and with physical exer-cise (L€oring et al. 2009; Wilkens et al. 2010; Vestbo et al.2013; Motamedi-Fakhr et al. 2016). Low inhalation airflow hasbeen shown to increase the particle delivery to the smallconductive airways. Slow inhalation was applied in modelingand experimental validation of particle deposition in patientswith chronic bronchitis (Svartengren et al. 2004). In anothermodel simulating bronchoconstriction, the breathing condi-tions under sitting and light exercise showed similar particledeposition patterns (Sturm 2013). Table 3 summarizes thevarious structural and functional properties relevant to COPDthat have been addressed in the existing in silicolung models.

Physiologically relevant modeling of the COPD lung

Based on the reviewed information it seems that in order tomimic physiologically relevant COPD lung for modeling ofparticle deposition the deposition in COPD lungs needs to bebetter understood and existing models have to be modifiedand validated against experimental data. Factors that requirefurther attention are: (i) heterogeneity and reduced airwaydiameters (to mimic bronchoconstriction), (ii) reduced num-ber of alveoli and increased volume per alveolus (to mimicemphysema), (iii) increased inspiratory airflow (i.e. shortenedinhalation time) and reduced expiratory airflow (i.e. pro-longed exhalation time) (to mimic altered breathing, particu-larly expiratory airflow limitation), (iv) inability of the lung toinflate upon inhalation and deflate upon exhalation (to mimiclung tissue fibrosis, loss of lung elasticity, and abnormal air-filled spaces), (v) slower mucociliary clearance, (vi) increasedtidal volume, and vii) site-specific deposition modeling. Weare unaware of any existing computational lung model thatallows for adjustment of all these factors. Modeling of par-ticle deposition in COPD is highly complex and requires closeinterdisciplinary collaboration. Figure 1 provides a summar-ized view of various factors for considerations to model aCOPD lung. Currently, there are limitations regarding bio-logical data for input as model parameters as well as experi-mental data on particle deposition for model validation. Bothtypes of data are required for better understanding of thedeposition pattern, especially in the COPD lung.

Perspective

Clinically potential domains currently considered for futuremanagement of COPD include: systemic and pulmonaryinflammation, lung microbiome, disease activity as well asimaging for emphysema, lung cancer, bronchiectasis andmolecular imaging (Agusti 2014). Thus, to achieve physiolo-gically relevant in silico modeling of the COPD lung,“inflammometry” (e.g. quantitative representation of inflam-matory cell recruitment) as well as data obtained from rapidlyevolving thoracic imaging techniques including low-dose CTscanners, SPECT, PET, and magnetic resonance imaging (MRI)as well as pulmonary mechanics data generated with theFOT/IOS technique need to be considered. In this context,several large COPD cohort studies may be useful. TheCOPDGene study (Regan et al. 2010) currently has an enroll-ment of over 10000 individuals’ and uses chest CT pheno-types including assessment of emphysema, gas trapping, andairway wall thickening for disease classification. TheEvaluation of COPD Longitudinally to Identify PredictiveSurrogate End-points) ECLIPSE; COPD subjects (GOLD catego-ries II–IV): 2180, smoking control: 343, and nonsmoking con-trol: 223) study also uses chest CT scans for diseaseclassification (Vestbo et al. 2008). Subpopulations and inter-mediate outcomes in COPD study (SPIROMICS; 3200 partici-pants) aimed to provide robust criteria for sub-classifyingCOPD consists of participants in four strata: severe COPD,mild/moderate COPD, smokers without airflow obstructionand nonsmoking controls with expiratory chest CT assess-ments (Couper et al. 2014). Therefore, quantitative imagingdata generated COPDGene, ECLIPSE, and SPIROMICS studiesmay provide important information for COPD-lung modelingpurposes (Agusti 2014; Sheikh et al. 2016). Access to quanti-tative information on small airway morphometry and damageat different COPD stages will greatly enhance the usefulnessof computational modeling to predict particle deposition inthe diseased lung. We hope that, in the near future, well-characterized studies for bronchiolar remodeling using quan-titative histology and micro-computed tomography, measure-ment of bronchiolar tissue volume, alveolar space, airwaysper generation, thickness of epithelial lining fluid, airway wallthickness and vasculature apart from detailed spirometry willbecome available. Such data will greatly enhance the possi-bilities to use and develop models that can describe and pre-dict particle deposition, distribution, and clearance ofparticles in the COPD lung. Still, additional knowledge isneeded to fully understand the pathophysiology of COPD inrelation to particle deposition. Such areas include regionalalterations in morphology resulting in regional differences inventilation and particle deposition, as well as changes inbreathing pattern, epithelial integrity, clearance capacity(mucociliary, macrophagic) and inflammatory cell recruitment.Therefore, it is also essential to generate experimental datain parallel with model development. To conclude, the fieldrequires close interdisciplinary collaborative work amongstexperimental, clinical, and computational fields to understandparticle deposition in COPD by addressing stage-specific dis-ease heterogeneity.

CRITICAL REVIEWS IN TOXICOLOGY 169

Acknowledgements

The authors gratefully acknowledge the constructive comments ofthe reviewers who were selected by the Editor and anonymous to theauthors. The comprehensive review process helped improve thefinal manuscript.

Financial support in terms of research grants from AstraZeneca (1November 2014), Vinnova (2016–01951) and the Swedish Heart LungFoundation (2018–0624; 2018–0325) are also acknowledged.

Declaration of interest

This review was developed as part of a joint research collaborationbetween AstraZeneca (AZ) and the Institute of Environmental Medicine(IMM), Karolinska Institutet regarding modeling of particle disposition inthe diseased lung.

The authors’ affiliations are as shown on the cover page. The authorsparticipated in the development of the paper as individual professionalsand not as representative of their employers. None of the authors havebeen involved in the last five years with regulatory or legal proceedingsrelated to the contents of the paper.

Prior to submission, the manuscript was submitted to AZ for internalreview for the sole purpose to check that the paper did not violate AZ’sproprietary rights or business secrets. The internal review was notintended to, and did not, result in any changes to the manuscript. Thecontents of the paper, including the conclusions drawn, are exclusivelythe views of the authors and not necessarily those of their employers.

The study was mainly financed by the above-mentioned researchgrants that covered the salary for Koustav Ganguly at IMM. Preparationof this review was conducted during the normal course of the author’semployment without external influence or support other than thereferred grants.

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