Multi-scale models of lung fibrosis

A Simple Framework for Agent-Based Modeling with Extracellular Matrix

Extracellular matrix (ECM) is a key component of the cellular microenvironment and critical in multiple disease and developmental processes. Representing ECM and cell-ECM interactions is a challenging multiscale problem as they span molecular-level details to tissue-level dynamics. While several computational frameworks exist for ECM modeling, they often focus on very detailed modeling of individual ECM fibers or represent only a single aspect of the ECM. Using the PhysiCell agent-based modeling platform, we developed a framework of intermediate detail with the ability to capture bidirectional cell-ECM interactions. We represent a small region of ECM, an ECM element, with three variables describing its local microstructure: anisotropy, density, and overall fiber orientation. To spatially model the ECM, we use an array of ECM elements. Cells remodel local ECM microstructure and in turn, local microstructure impacts cellular motility. We demonstrate the utility of this framework and reusability of its core cell-ECM interaction model through examples in cellular invasion, wound healing, basement membrane degradation, and leader-follower collective migration. Despite the relative simplicity of the framework, it is able to capture a broad range of cell-ECM interactions of interest to the modeling community. Furthermore, variables representing the ECM microstructure are accessible through simple programming interfaces. This allows them to impact cell behaviors, such as proliferation and death, without requiring custom code for each interaction, particularly through PhysiCell's modeling grammar, enabling rapid modeling of a diverse range of cell-matrix biology. We make this framework available as a free and open source software package at https://github.com/PhysiCell-Models/collective-invasion .

Multiscale computational model predicts how environmental changes and treatments affect microvascular remodeling in fibrotic disease

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired and lung function declines. We integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (EC) and pericytes, the cells that comprise microvessels. Nintedanib, an Food and Drug Administration-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can predict and explain how cell decisions affect tissue changes during disease and in response to treatments.

Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers

Neuroendocrine tumors (NETs) occur sporadically or as part of rare endocrine tumor syndromes (RETSs) such as multiple endocrine neoplasia 1 and von Hippel-Lindau syndromes. Due to their relative rarity and lack of model systems, NETs and RETSs are difficult to study, hindering advancements in therapeutic development. Causal or mechanistic mathematical modeling is widely deployed in disease areas such as breast and prostate cancers, aiding the understanding of observations and streamlining in vitro and in vivo modeling efforts. Mathematical modeling, while not yet widely utilized in NET research, offers an opportunity to accelerate NET research and therapy development. To illustrate this, we highlight examples of how mathematical modeling associated with more common endocrine cancers has been successfully used in the preclinical, translational and clinical settings. We also provide a scope of the limited work that has been done in NETs and map how these techniques can be utilized in NET research to address specific outstanding challenges in the field. Finally, we include practical details such as hardware and data requirements, present advantages and disadvantages of various mathematical modeling approaches and discuss challenges of using mathematical modeling. Through a cross-disciplinary approach, we believe that many currently difficult problems can be made more tractable by applying mathematical modeling and that the field of rare diseases in endocrine oncology is well poised to take advantage of these techniques.

Alveolar wall hyperelastic material properties determined using alveolar cluster model with experimental stress-stretch and pressure-volume data

Micro-scale models of lung tissue have been employed by researchers to investigate alveolar mechanics; however, they have been limited by the lack of biofidelic material properties for the alveolar wall. To address this challenge, a finite element model of an alveolar cluster was developed comprising a tetrakaidecahedron array with the nominal characteristics of human alveolar structure. Lung expansion was simulated in the model by prescribing a pressure and monitoring the volume, to produce a pressure-volume (PV) response that could be compared to experimental PV data. The alveolar wall properties in the model were optimized to match experimental PV response of lungs filled with saline, to eliminate surface tension effects and isolate the alveolar wall tissue response. When simulated in uniaxial tension, the model was in agreement with reported experimental properties of uniaxial tension on excised lung tissue. The work presented herein was able to link micro-scale alveolar response to two disparate macroscopic experimental datasets (stress-stretch and PV response of lung) and presents hyperelastic properties of the alveolar wall for use in alveolar scale finite element models and multi-scale models. Future research will incorporate surface tension effects, and investigate alveolar injury mechanisms.

Personalized in silico model for radiation-induced pulmonary fibrosis

Radiation-induced pulmonary fibrosis (RIPF) is a severe late-stage complication of radiotherapy (RT) to the chest area, typically used in lung cancer treatment. This condition is characterized by the gradual and irreversible replacement of healthy lung tissue with fibrous scar tissue, leading to decreased lung function, reduced oxygen exchange and critical respiratory deficiencies. Currently, predicting and managing lung fibrosis post-RT remains challenging, with limited preventive and treatment options. Accurate prediction of fibrosis onset and progression is therefore clinically crucial. We present a personalized in silico model for pulmonary fibrosis that encompasses tumour regression, fibrosis development and lung tissue remodelling post-radiation. Our continuum-based model was developed using data from 12 RT-treated lung cancer patients and integrates computed tomography (CT) and dosimetry data to simulate the spatio-temporal evolution of fibrosis. We demonstrate the ability of the in silico model to capture the extent of fibrosis in the entire cohort with a less than 1% deviation from clinical observations, in addition to providing quantitative metrics of spatial similarity. These findings underscore the potential of the model to improve treatment planning and risk assessment, paving the way for more personalized and effective management of RIPF.

A rule-based multiscale model of hepatic stellate cell plasticity: Critical role of the inactivation loop in fibrosis progression

Hepatic stellate cells (HSC) are the source of extracellular matrix (ECM) whose overproduction leads to fibrosis, a condition that impairs liver functions in chronic liver diseases. Understanding the dynamics of HSCs will provide insights needed to develop new therapeutic approaches. Few models of hepatic fibrosis have been proposed, and none of them include the heterogeneity of HSC phenotypes recently highlighted by single-cell RNA sequencing analyses. Here, we developed rule-based models to study HSC dynamics during fibrosis progression and reversion. We used the Kappa graph rewriting language, for which we used tokens and counters to overcome temporal explosion. HSCs are modeled as agents that present seven physiological cellular states and that interact with (TGFβ1) molecules which regulate HSC activation and the secretion of type I collagen, the main component of the ECM. Simulation studies revealed the critical role of the HSC inactivation process during fibrosis progression and reversion. While inactivation allows elimination of activated HSCs during reversion steps, reactivation loops of inactivated HSCs (iHSCs) are required to sustain fibrosis. Furthermore, we demonstrated the model's sensitivity to (TGFβ1) parameters, suggesting its adaptability to a variety of pathophysiological conditions for which levels of (TGFβ1) production associated with the inflammatory response differ. Using new experimental data from a mouse model of CCl4-induced liver fibrosis, we validated the predicted ECM dynamics. Our model also predicts the accumulation of iHSCs during chronic liver disease. By analyzing RNA sequencing data from patients with non-alcoholic steatohepatitis (NASH) associated with liver fibrosis, we confirmed this accumulation, identifying iHSCs as novel markers of fibrosis progression. Overall, our study provides the first model of HSC dynamics in chronic liver disease that can be used to explore the regulatory role of iHSCs in liver homeostasis. Moreover, our model can also be generalized to fibroblasts during repair and fibrosis in other tissues.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired due to loss of alveolar structures and lung function declines. Here, we integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition (PMT) in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can explain how cell decisions affect tissue changes during disease and in response to treatments.

The evolution of in vitro models of lung fibrosis: promising prospects for drug discovery

Lung fibrosis is a complex process, with unknown underlying mechanisms, involving various triggers, diseases and stimuli. Different cell types (epithelial cells, endothelial cells, fibroblasts and macrophages) interact dynamically through multiple signalling pathways, including biochemical/molecular and mechanical signals, such as stiffness, affecting cell function and differentiation. Idiopathic pulmonary fibrosis (IPF) is the most common fibrosing interstitial lung disease (fILD), characterised by a notably high mortality. Unfortunately, effective treatments for advanced fILD, and especially IPF and non-IPF progressive fibrosing phenotype ILD, are still lacking. The development of pharmacological therapies faces challenges due to limited knowledge of fibrosis pathogenesis and the absence of pre-clinical models accurately representing the complex features of the disease. To address these challenges, new model systems have been developed to enhance the translatability of preclinical drug testing and bridge the gap to human clinical trials. The use of two- and three-dimensional in vitro cultures derived from healthy or diseased individuals allows for a better understanding of the underlying mechanisms responsible for lung fibrosis. Additionally, microfluidics systems, which replicate the respiratory system's physiology ex vivo, offer promising opportunities for the development of effective therapies, especially for IPF.

Comparison of optimization parametrizations for regional lung compliance estimation using personalized pulmonary poromechanical modeling

Interstitial lung diseases, such as idiopathic pulmonary fibrosis (IPF) or post-COVID-19 pulmonary fibrosis, are progressive and severe diseases characterized by an irreversible scarring of interstitial tissues that affects lung function. Despite many efforts, these diseases remain poorly understood and poorly treated. In this paper, we propose an automated method for the estimation of personalized regional lung compliances based on a poromechanical model of the lung. The model is personalized by integrating routine clinical imaging data - namely computed tomography images taken at two breathing levels in order to reproduce the breathing kinematic-notably through an inverse problem with fully personalized boundary conditions that is solved to estimate patient-specific regional lung compliances. A new parametrization of the inverse problem is introduced in this paper, based on the combined estimation of a personalized breathing pressure in addition to material parameters, improving the robustness and consistency of estimation results. The method is applied to three IPF patients and one post-COVID-19 patient. This personalized model could help better understand the role of mechanics in pulmonary remodeling due to fibrosis; moreover, patient-specific regional lung compliances could be used as an objective and quantitative biomarker for improved diagnosis and treatment follow up for various interstitial lung diseases.

Recent trends in the nanozeolites-based oxygen concentrators and their application in respiratory disorders

Medical-grade oxygen is the basic need for all medical complications, especially in respiratory-based discomforts. There was a drastic increase in the demand for medical-grade oxygen during the current pandemic. The non-availability of medical-grade oxygen led to several complications, including death. The oxygen concentrator was only the last hope for the patient during COVID-19 pandemic around the globe. The demands also are everlasting during other microbial respiratory infections. The yield of oxygen using conventional molecular zeolites in the traditional oxygen concentrator process is less than the yield noticed when its nano-form is used. Nanotechnology has enlightened hope for the efficient production of oxygen by such oxygen concentrators. Here in the current review work, the authors have highlighted the basic structural features of oxygen concentrators along with the current working principle. Besides, it has been tried to bridge the gap between conventional oxygen concentrators and advanced ones by using nanotechnology. Nanoparticles being usually within 100 nm in size have a high surface area to volume ratio, which makes them suitable adsorbents for oxygen. Here authors have suggested the use of nano zeolite in place of molecular zeolites in the oxygen concentrator for efficient delivery of oxygen by the oxygen concentrators.

Computational lung modelling in respiratory medicine

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure-function relationship in the lung.

Multiscale modeling in disease

Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.

Mechano-chemo signaling interactions modulate matrix production by cardiac fibroblasts

Extracellular matrix remodeling after myocardial infarction occurs in a dynamic environment in which local mechanical stresses and biochemical signaling species stimulate the accumulation of collagen-rich scar tissue. It is well-known that cardiac fibroblasts regulate post-infarction matrix turnover by secreting matrix proteins, proteases, and protease inhibitors in response to both biochemical stimuli and mechanical stretch, but how these stimuli act together to dictate cellular responses is still unclear. We developed a screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical agonists and cyclic uniaxial stretch in order to elucidate the relationships between stretch, biochemical signaling, and cardiac matrix turnover. We found that stretch significantly synergized with biochemical agonists to inhibit the secretion of matrix metalloproteinases, with stretch either amplifying protease suppression by individual agonists or antagonizing agonist-driven upregulation of protease expression. Stretch also modulated fibroblast sensitivity towards biochemical agonists by either sensitizing cells towards agonists that suppress protease secretion or de-sensitizing cells towards agonists that upregulate protease secretion. These findings suggest that the mechanical environment can significantly alter fibrosis-related signaling in cardiac fibroblasts, suggesting caution when extrapolating in vitro data to predict effects of fibrosis-related cytokines in situations like myocardial infarction where mechanical stretch occurs.

Mycobacterium tuberculosis Load in Host Cells and the Antibacterial Activity of Alveolar Macrophages Are Linked and Differentially Regulated in Various Lung Lesions of Patients with Pulmonary Tuberculosis

Tuberculosis (TB) is a disease caused by Mycobacterium tuberculosis (Mtb) infection with the formation of a broad range of abnormal lung lesions within a single patient. Although host-pathogen interactions determine disease outcome, they are poorly understood within individual lesions at different stages of maturation. We compared Mtb load in a tuberculoma wall and the lung tissue distant from tuberculomas in TB patients. These data were combined with an analysis of activation and bactericidal statuses of alveolar macrophages and other cell subtypes examined both in ex vivo culture and on the histological sections obtained from the same lung lesions. The expression of pattern recognition receptors CD14, CD11b, and TLR-2, transcription factors HIF-1α, HIF-2α, and NF-κB p50 and p65, enzymes iNOS and COX-2, reactive oxygen species (ROS) biosynthesis, and lipid production were detected for various lung lesions, with individual Mtb loads in them. The walls of tuberculomas with insufficient inflammation and excessive fibrosis were identified as being the main niche for Mtb survival (single or as colonies) in non-foamy alveolar macrophages among various lung lesions examined. The identification of factors engaged in the control of Mtb infection and tissue pathology in local lung microenvironments, where host-pathogen relationships take place, is critical for the development of new therapeutic strategies.

Elevated Serum Amyloid a Levels Are not Specific for Sarcoidosis but Associate with a Fibrotic Pulmonary Phenotype

Elevated Serum Amyloid A (SAA) levels have been found in several inflammatory diseases, including sarcoidosis. SAA is suggested to be involved in sarcoidosis pathogenesis by involvement in granuloma formation and maintenance. We hypothesized that SAA serum levels would be higher in sarcoidosis compared to other non-infectious granulomatous and non-granulomatous diseases. SAA levels were measured in serum from sarcoidosis, Hypersensitivity pneumonitis (HP), and (eosinophilic) granulomatosis with polyangiitis ((E)GPA) patients. Idiopathic pulmonary fibrosis (IPF) patients were included as non-granulomatous disease group. SAA levels of patients with sarcoidosis (31.0 µg/mL), HP (23.4 µg/mL), (E)GPA (36.9 µg/mL), and IPF (22.1 µg/mL) were all higher than SAA levels of healthy controls (10.1 µg/mL). SAA levels did not differ between the diagnostic groups. When SAA serum levels were analyzed in sarcoidosis subgroups, fibrotic sarcoidosis patients showed higher SAA levels than sarcoidosis patients without fibrosis (47.8 µg/mL vs. 29.4 µg/mL, p = 0.005). To conclude, the observation that fibrotic sarcoidosis patients have higher SAA levels, together with our finding that SAA levels were also increased in IPF patients, suggests that SAA may next to granulomatous processes also reflect the process of fibrogenesis. Further studies should clarify the exact role of SAA in fibrosis and the underlying mechanisms involved.

Genetic Delivery and Gene Therapy in Pulmonary Hypertension

Pulmonary hypertension (PH) is a progressive complex fatal disease of multiple etiologies. Hyperproliferation and resistance to apoptosis of vascular cells of intimal, medial, and adventitial layers of pulmonary vessels trigger excessive pulmonary vascular remodeling and vasoconstriction in the course of pulmonary arterial hypertension (PAH), a subgroup of PH. Multiple gene mutation/s or dysregulated gene expression contribute to the pathogenesis of PAH by endorsing the proliferation and promoting the resistance to apoptosis of pulmonary vascular cells. Given the vital role of these cells in PAH progression, the development of safe and efficient-gene therapeutic approaches that lead to restoration or down-regulation of gene expression, generally involved in the etiology of the disease is the need of the hour. Currently, none of the FDA-approved drugs provides a cure against PH, hence innovative tools may offer a novel treatment paradigm for this progressive and lethal disorder by silencing pathological genes, expressing therapeutic proteins, or through gene-editing applications. Here, we review the effectiveness and limitations of the presently available gene therapy approaches for PH. We provide a brief survey of commonly existing and currently applicable gene transfer methods for pulmonary vascular cells in vitro and describe some more recent developments for gene delivery existing in the field of PH in vivo.

Serum calprotectin as new biomarker for disease severity in idiopathic pulmonary fibrosis: a cross-sectional study in two independent cohorts

BACKGROUND

Non-invasive biomarkers for the assessment of disease severity in idiopathic pulmonary fibrosis (IPF) are urgently needed. Calprotectin belongs to the S-100 proteins produced by neutrophils, which likely contribute to IPF pathogenesis. Calprotectin is a well-established biomarker in inflammatory bowel diseases. In this cross-sectional study, we aimed to establish the potential role of calprotectin as a biomarker in IPF. Specifically, we hypothesised that patients with IPF have higher serum calprotectin levels compared with healthy controls, and that calprotectin levels are associated with disease severity.

METHODS

Blood samples were obtained from healthy volunteers (n=26) and from two independent IPF cohorts (derivation cohort n=26, validation cohort n=66). Serum calprotectin levels were measured with a commercial kit adapted for that purpose and compared between healthy controls and patients with IPF. Clinical parameters, including forced vital capacity, diffusing capacity for carbon monoxide (DLCO) and the Composite Physiologic Index (CPI), were correlated with calprotectin serum levels.

RESULTS

The IPF derivation cohort showed increased serum calprotectin levels compared with healthy controls (2.47±1.67 vs 0.97±0.53 µg/mL, p<0.001). In addition, serum calprotectin levels correlated with DLCO% predicted (r=-0.53, p=0.007) and with CPI (r=0.66, p=0.007). These findings were confirmed in an independent IPF validation cohort.

CONCLUSION

Serum calprotectin levels are significantly increased in patients with IPF compared with healthy controls and correlate with DLCO and CPI. Calprotectin might be a potential new biomarker for disease severity in IPF.

Fibroblasts: The arbiters of extracellular matrix remodeling

Extracellular matrix (ECM) is the foundation on which all cells and organs converge to orchestrate normal physiological functions. In the setting of pathology, the ECM is modified to incorporate additional roles, with modifications including turnover of existing ECM and deposition of new ECM. The fibroblast is center stage in coordinating both normal tissue homeostasis and response to disease. Understanding how fibroblasts work under normal conditions and are activated in response to injury or stress will provide mechanistic insight that triggers discovery of new therapeutic treatments for a wide range of disease. We highlight here fibroblast roles in the cancer, lung, and heart as example systems where fibroblasts are major contributors to homeostasis and pathology.