Three-dimensional model of surfactant replacement therapy

Surfactant replacement therapy (SRT) involves instillation of a liquid-surfactant mixture directly into the lung airway tree. It is widely successful for treating surfactant deficiency in premature neonates who develop neonatal respiratory distress syndrome (NRDS). However, when applied to adults with acute respiratory distress syndrome (ARDS), early successes were followed by failures. This unexpected and puzzling situation is a vexing issue in the pulmonary community. A pressing question is whether the instilled surfactant mixture actually reaches the adult alveoli/acinus in therapeutic amounts. In this study, to our knowledge, we present the first mathematical model of SRT in a 3D lung structure to provide insight into answering this and other questions. The delivery is computed from fluid mechanical principals for 3D models of the lung airway tree for neonates and adults. A liquid plug propagates through the tree from forced inspiration. In two separate modeling steps, the plug deposits a coating film on the airway wall and then splits unevenly at the bifurcation due to gravity. The model generates 3D images of the resulting acinar distribution and calculates two global indexes, efficiency and homogeneity. Simulating published procedural methods, we show the neonatal lung is a well-mixed compartment, whereas the adult lung is not. The earlier, successful adult SRT studies show comparatively good index values implying adequate delivery. The later, failed studies used different protocols resulting in very low values of both indexes, consistent with inadequate acinar delivery. Reasons for these differences and the evolution of failure from success are outlined and potential remedies discussed.Since the early 1980s, surfactant replacement therapy (SRT) has been successful in applications to prematurely born neonates to treat their lack of surfactant production, which normally initiates late in gestation (1). Because surfactant reduces the surface tension between the air and the lung’s liquid lining, its deficiency creates high surface tensions and collapsed, stiff lungs making them difficult to inflate. The resulting clinical entity of labored breathing and poor oxygenation is called neonatal respiratory distress syndrome (NRDS), or hyaline membrane disease, and is a risk of premature birth increasing with decreasing gestational age. The incidence is ∼1% of all births, equating to 40,000 cases annually in the United States (2). The mortality associated with NRDS dropped from 4,997 deaths in 1980 to 861 in 2005, and SRT played an important role in this success (3).SRT has also been tried in adults whose surfactant systems are compromised by acute respiratory distress syndrome (ARDS). ARDS results from overwhelming infections, mechanical injuries, and other insults either directly or indirectly to the lung. ARDS cases in the United States total 190,600 annually with a 39% mortality rate yielding 74,500 deaths (4). Although some early SRT large-animal and adult clinical trials were successful (5–8), subsequent studies were failures (9–11). The field is looking for direction, with concerns ranging from the delivered surfactant biochemistry to the persistence of the underlying ARDS disease to the adequacy of delivery (12). The model presented here addresses the delivery issue and shows that simulations of these adult SRT studies produce significantly different delivery distributions that can explain success vs. failure.Effective drug delivery is a major medical challenge. First mathematical models for i.v. (13), oral (14), and aerosol (15) modalities contributed to the development of those fields simulating drug uptake, distribution, metabolism, and elimination. The role of models is to interpret data mechanistically, predict outcomes, follow treatment courses, relate dose to response, establish safety criteria, design new drugs and delivery strategies, compare animal experiments to human applications, and tailor protocols to patient specific circumstances. To our knowledge, the work presented here is the first structural model of SRT with the same goals. It quantifies the physics of two-phase fluid flow, air and liquid, into a branching network of airways representing the lung (16–18). Our model provides a mechanistic foundation for SRT delivery, which turns out to be a highly nonlinear process. A working SRT model can have a significant impact on this field, moving it from a process of informed trial and error to one that harnesses the underlying fundamental mechanisms.In SRT, the instilled mixture can form a liquid plug in the trachea (19), which then propagates through the tracheobronchial tree by forced inspiratory airflow. As the liquid plug propagates distally, it coats the airway walls, losing some of its mass, and also splits at bifurcations. Our mathematical model considers these two important steps in sequence. For step A (airway), the propagating plug deposits some content onto the airway walls into a trailing film. This deposition, or coating, reduces efficiency because less surfactant mixture reaches the acinus. Our experimentally validated theory predicts the trailing film thickness as a function of plug viscosity, speed, surface tension, and tube radius from computational fluid dynamics results (17). Then, in step B (bifurcation), the plug splits at an airway bifurcation in an uneven manner due to gravity, favoring the steepest downhill daughter tube, so reduces distribution homogeneity. The larger the structure (adult lungs), the greater is this reduction. For this step, we compute the ratio of the split volumes from conservation of momentum and mass. This split ratio depends on the plug speed, viscosity, surface tension, bifurcation geometry, and orientation with gravity (20, 21). Each step is performed sequentially through a 3D tree construct with the geometrical features of the conducting airways (22, 23) (Materials and Methods). The time period for the flow is a single forced inspiration. A larger fluid velocity yields more even splitting, improving homogeneity, but leaves a thicker wall deposition layer, reducing efficiency. The resulting distribution of SRT depends on the competition between these two fluid mechanical phenomena.