Patient-specific modeling of regional antibiotic concentration levels in airways of patients with cystic fibrosis: are we dosing high enough?

Abstract

Background: Pseudomonas aeruginosa (Pa) infection is an important contributor to the progression of cystic fibrosis (CF) lung disease. The cornerstone treatment for Pa infection is the use of inhaled antibiotics. However, there is substantial lung disease heterogeneity within and between patients that likely impacts deposition patterns of inhaled antibiotics. Therefore, this may result in airways below the minimal inhibitory concentration of the inhaled agent. Very little is known about antibiotic concentrations in small airways, in particular the effect of structural lung abnormalities. We therefore aimed to develop a patient-specific airway model to predict concentrations of inhaled antibiotics and to study the impact of structural lung changes and breathing profile on local concentrations in airways of patients with CF.

Methods: In- and expiratory CT-scans of children with CF (5-17 years) were scored (CF-CT score), segmented and reconstructed into 3D airway models. Computational fluid dynamic (CFD) simulations were performed on 40 airway models to predict local Aztreonam lysine for inhalation (AZLI) concentrations. Patient-specific lobar flow distribution and nebulization of 75 mg AZLI through a digital Pari eFlow model with mass median aerodynamic diameter range were used at the inlet of the airway model. AZLI concentrations for central and small airways were computed for different breathing patterns and airway surface liquid thicknesses.

Results: In most simulated conditions, concentrations in both central and small airways were well above the minimal inhibitory concentration. However, small airways in more diseased lobes were likely to receive suboptimal AZLI. Structural lung disease and increased tidal volumes, respiratory rates and larger particle sizes greatly reduced small airway concentrations.

Conclusions: CFD modeling showed that concentrations of inhaled antibiotic delivered to the small airways are highly patient specific and vary throughout the bronchial tree. These results suggest that anti-Pa treatment of especially the small airways can be improved.

Conflict of interest statement

Competing Interests: This was an investigator initiated study. FluidDA nv received an unconditional grant from Gilead Sciences Inc. Author Cedric van Holsbeke and Wim G. Vos are employed by FluidDA nv. J. de Backer and Wim G. Vos are shareholders of FluidDA NV, a company that commercializes some of the techniques used in this manuscript. FluidDA NV covered the salary costs of the PhD student (AB) for the length of this study. HJ reports grants from Philips Respironics, grants from Novartis, grants from Chiesi, other from Teva Pharma, other from Philips Respironics, and other from Gilead, outside the submitted work. HT reports grants from Gilead Sciences Inc, during the conduct of the study; other from Roche, other from Pharmaxis, other from Novartis, grants from CFF, and grants from Vertex, outside the submitted work. In addition, Dr. Tiddens has the following patent ActivAero licensed (Improved Method for Treatment of Patients with Cystic Fibrosis, Patent No: 10763129.3-2112). All financial aspects of the above mentioned activities are handled by the BV Kindergeneeskunde of the ErasmusMC Sophia Children’s Hospital. There are no further patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Fig 1. Coupled mouthpiece/upper/lower airway model.

Coupled mouthpiece/upper/lower airway model subdivided in multiple regions. Airways are segmented up to the 5th-9th generation.

Fig 2. Comparison of CF-CT subscores per lobe.

Comparison of CF-CT subscores per lobe, presented as % of max CF-CT score. Data are presented as median (range), unless otherwise indicated. White bars represent bronchiectasis score, light grey bars represent airway wall thickening score and dark grey bars represent air trapping score. RUL = right upper lobe (n = 22), RML = right middle lobe (n = 22), RLL = right lower lobe (n = 40), LUL = left upper lobe (n = 39), LLL = left lower lobe (n = 39).

Fig 3. Differences between lobes in AZLI concentrations.

Differences between lobes in AZLI concentrations for the scenario of thick airway surface liquid with largest aerosol diameter. Data are presented as median (range), unless otherwise indicated. Significant differences in AZLI concentrations were found between all lobes, except for one pairwise comparison (see S1 Table). RUL = right upper lobe, RML = right middle lobe. RLL = right lower lobe, LUL = left upper lobe, LLL = left lower lobe.

Fig 4. Percentage area of small airways with AZLI 90.

Percentage area of small airways with AZLI concentrations 90. Data are presented as median (range) for the different scenarios. White bars represent the smallest aerosol diameter (2.9 μm), light grey bars represent the median aerosol diameter (3.18 μm) and dark grey bars represent the largest aerosol diameter (4.35 μm). ASL = airway surface liquid.

Fig 5. Relative AZLI concentrations in central and small airways of 2 patients for 3 different scenarios.

Simulations of AZLI deposition in 2 patients, representing 3 scenarios of varied airway surface liquid thickness (ASL) and aerosol diameter. Severity of CF lung disease was determined by the CF-CT score (% of total CF-CT score). Scenario a = thin ASL with smallest aerosol diameter; scenario b = median ASL with median aerosol diameter; scenario c = thick ASL with largest aerosol diameter. Part 1a, 1b and 1c: Patient 1, mild CF lung disease: bronchiectasis 0.0%, airway wall thickening 0.0% and air trapping 11.1%. Patient 1 received concentrations > 10xMIC90 in the central and small airways independent of ASL thickness and aerosol diameter (Part 1a, 1b, 1c). Part 2a, 2c and 2c: Patient 2, more severe lung disease: bronchiectasis 12.5%, airway wall thickening 11.1% and air trapping 38.9%. Patient 2 received concentrations > 10xMIC90 in the central and small airways in scenario a and b (Part 2a and 2b), but AZLI concentrations < 10xMIC90 in the small airways in scenario c (right upper and middle lobes) (Part 2c).

Fig 6. Influence of inhalation technique on AZLI concentrations.

Influence of inhalation technique on AZLI concentrations presented as percentage area of small airways with AZLI 90. Low breathing profile: tidal volume of 6 ml/kg (228 ml) and respiration rate of 14 breaths/min. Average breathing profile: tidal volume of 10 ml/kg (380 ml) and respiration rate of 18 breaths/min. High breathing profile: tidal volume of 14 ml/kg (532 ml) and respiration rate of 22 breaths/min. Data are presented as median (range) for the different scenarios. Light grey bars represent the scenario of median ASL (5 μm) with largest aerosol diameter (4.35 μm). The darker grey bars represent the scenario of thick ASL (7 μm) with median aerosol diameter (3.18 μm) and the darkest grey bars represent the scenario of thick ASL (7 μm) with largest aerosol diameter (4.35 μm). The scenarios of thin ASL with all diameters, median ASL with smallest and median diameter and thick ASL with smallest diameter are not represented as all breathing profiles resulted in AZLI concentrations above 10xMIC90. ASL = airway surface liquid.