Abstract Rationale Accurately predicting bronchodilator performance remains a major challenge in inhaled therapeutics. We developed a coupled computational fluid and particle dynamics (CFPD) and physiologically based pharmacokinetics/pharmacodynamics (PBPK/PD) framework that leverages subject-specific, CT-derived airway geometries and regional ventilation to predict lung exposure and bronchodilator response, quantified as the change in Forced Expiratory Volume in 1 second (ΔFEV1). Methods Airway trees were extracted from CT scans at full inspiration and propagated distally using a volume-filling algorithm to generate a generation-resolved 1D airway tree, including tracheobronchial and alveolar regions. Two cohorts were included in this study: asthmatic (n = 6) and healthy (n = 10) subjects. CFPD simulations predicted subject-specific airflow and drug deposition by generation (G) for inhalation of 400 μg albuterol under two device/inhalation profiles: metered-dose inhaler (MDI)/(slow and deep, SD) and dry powder inhaler (DPI)/(quick and deep, QD). Deposited doses were input into the PBPK model at the epithelial lining fluid (ELF), coupled to whole-body distribution and blood clearance, to predict plasma and tissue concentrations. The PD model was then implemented to predict ΔFEV1 at the sub-epithelium effect site. Results Validations against literature data showed good agreement for plasma and ELF concentrations following albuterol inhalation via MDI, as well as for the ΔFEV1 time course over 24 hours. Two key patterns emerged: (i) SD inhalations shifted the dose to the alveolar region, sustaining high drug concentrations in the distal ELF, whereas QD inhalation deposited albuterol in lung generations G2-G6; (ii) asthmatic subjects exhibited higher resistance vs. healthy subjects in G5-G10, amplifying the effect of short-acting β2-agonist (SABA) dilation and explaining larger early ΔFEV1. PD model parameters, fitted by least-squares to the observed ΔFEV1 experimental data, resulted in a half-maximal effective concentration (EC50 =1.1±0.7 nM) and Hill slope (n = 1.6±0.1) with R2= 0.98. This framework mechanistically links inhaler type to clinical benefit and supports patient-specific treatment in asthmatics. Conclusion A subject-specific CFPD model integrated with a PBPK/PD framework was developed to predict inhaled albuterol exposure in plasma and ELF, as well as clinical response (ΔFEV1), based on CT-derived airway structure, lung ventilation, and inhalation profile. This approach has the potential to guide inhaler selection, dosing schedules, and disease-informed inhalational treatment planning across a range of respiratory diseases. Funding Sources NIH Grant R01-HL168116, P30 ES005605, and ED P116S21000 This abstract is funded by: NIH and Department of Education
Rajaraman et al. (Fri,) studied this question.
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