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Surfactant delivery in rat lungs: Comparing 3D geometrical simulation model with experimental instillation

Authors :
Joshua Satalin
Bruno Louis
Marcel Filoche
James B. Grotberg
Gary F. Nieman
S. Baker
Louis A. Gatto
Alireza Kazemi
Daniel Isabey
Department of Electrical Engineering (DEE)
Ohio State University [Columbus] (OSU)
IMRB - 'Biomechanics and Respiratory Apparatus' [Créteil] (U955 Inserm - UPEC)
Institut Mondor de Recherche Biomédicale (IMRB)
Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)
Department of Biomedical Engineering [Ann Arbor, MI, États-Unis]
University of Michigan [Ann Arbor]
University of Michigan System-University of Michigan System
Laboratoire de physique de la matière condensée (LPMC)
École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)
Source :
PLoS Computational Biology, PLoS Computational Biology, Public Library of Science, 2019, 15 (10), pp.e1007408. ⟨10.1371/journal.pcbi.1007408⟩, PLoS Computational Biology, Vol 15, Iss 10, p e1007408 (2019)
Publication Year :
2019
Publisher :
Public Library of Science (PLoS), 2019.

Abstract

Surfactant Replacement Therapy (SRT), which involves instillation of a liquid-surfactant mixture directly into the lung airway tree, is a major therapeutic treatment in neonatal patients with respiratory distress syndrome (RDS). This procedure has proved to be remarkably effective in premature newborns, inducing a five-fold decrease of mortality in the past 35 years. Disappointingly, its use in adults for treating acute respiratory distress syndrome (ARDS) experienced initial success followed by failures. Our recently developed numerical model has demonstrated that transition from success to failure of SRT in adults could, in fact, have a fluid mechanical origin that is potentially reversible. Here, we present the first numerical simulations of surfactant delivery into a realistic asymmetric conducting airway tree of the rat lung and compare them with experimental results. The roles of dose volume (VD), flow rate, and multiple aliquot delivery are investigated. We find that our simulations of surfactant delivery in rat lungs are in good agreement with our experimental data. In particular, we show that the monopodial architecture of the rat airway tree plays a major role in surfactant delivery, contributing to the poor homogeneity of the end distribution of surfactant. In addition, we observe that increasing VD increases the amount of surfactant delivered to the acini after losing a portion to coating the involved airways, the coating cost volume, VCC. Finally, we quantitatively assess the improvement resulting from a multiple aliquot delivery, a method sometimes employed clinically, and find that a much larger fraction of surfactant reaches the alveolar regions in this case. This is the first direct qualitative and quantitative comparison of our numerical model with experimental studies, which enhances our previous predictions in adults and neonates while providing a tool for predicting, engineering, and optimizing patient-specific surfactant delivery in complex situations.<br />Author summary Surfactant Replacement Therapy (SRT), which involves instillation of a liquid-surfactant mixture directly into the lung airway tree, is a major therapeutic treatment in neonatal patients with respiratory distress syndrome (RDS). This procedure has contributed to a major decrease of the infant mortality in the past 35 years. Disappointingly, its use in adults for treating acute respiratory distress syndrome (ARDS) experienced initial success followed by failures. In this article, we present the first numerical simulations of surfactant delivery into realistic models of the conducting airway tree of the rat lung and compare them with experimental results. In particular, we show that the monopodial architecture of the rat airway tree plays a major role in surfactant delivery, contributing to the poor homogeneity of the end distribution of surfactant. This is the first direct qualitative and quantitative comparison of our numerical model with experimental studies, which enhances our previous predictions in adults and neonates while providing a tool for predicting, engineering, and optimizing patient-specific surfactant delivery in complex situations.

Subjects

Subjects :
0301 basic medicine
Models, Anatomic
ARDS
Critical Care and Emergency Medicine
Airway tree
Pulmonology
Surfactants
Respiratory System
[SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract
Rats, Sprague-Dawley
0302 clinical medicine
Pulmonary surfactant
Medicine and Health Sciences
Biology (General)
Materials
Acute Respiratory Distress Syndrome
Lung
Flow Rate
Ecology
Respiratory distress
Physics
Simulation and Modeling
Classical Mechanics
respiratory system
3. Good health
Trachea
medicine.anatomical_structure
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Engineering and Technology
Anatomy
[PHYS.COND.CM-SCM]Physics [physics]/Condensed Matter [cond-mat]/Soft Condensed Matter [cond-mat.soft]
Research Article
QH301-705.5
Therapeutic treatment
Materials Science
Acute respiratory distress
Fluid Mechanics
Research and Analysis Methods
Continuum Mechanics
03 medical and health sciences
Cellular and Molecular Neuroscience
Surface-Active Agents
Respiratory Failure
Coatings
Genetics
medicine
Distribution (pharmacology)
Surface Tension
Animals
Computer Simulation
Rats, Long-Evans
[PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]
Rats, Wistar
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Maximal Expiratory Flow Rate
Models, Statistical
business.industry
Surface Treatments
Biology and Life Sciences
Neonates
Fluid Dynamics
Pulmonary Surfactants
medicine.disease
respiratory tract diseases
Rats
030104 developmental biology
Manufacturing Processes
[SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic
Hydrodynamics
business
030217 neurology & neurosurgery
Biomedical engineering
Developmental Biology

Details

ISSN :
1553734X and 15537358
Database :
OpenAIRE
Journal :
PLoS Computational Biology, PLoS Computational Biology, Public Library of Science, 2019, 15 (10), pp.e1007408. ⟨10.1371/journal.pcbi.1007408⟩, PLoS Computational Biology, Vol 15, Iss 10, p e1007408 (2019)
Accession number :
edsair.doi.dedup.....68aea35e0b4bc548b30276540deb98c0
Full Text :
https://doi.org/10.7302/6626