Subject-Specific Whole-Lung CFPD Coupled with PBPK/PD to Predict Inhaled Bronchodilator Response in Asthmatic and...

Accurate prediction of bronchodilator performance remains challenging. We present a coupled computational fluid and particle dynamics (CFPD) and physiologically-based pharmacokinetic/pharmacodynamics (PBPK/PD) framework that leverages subject-specific CT-derived airway trees and airflow distributions to model regional lung exposure and the resulting clinical effect, quantified as the change in Forced Expiratory Volume in 1 second (ΔFEV₁). Data from six asthmatic and ten healthy subjects were analyzed. CFPD simulations predicted subject-specific airflow and drug deposition by airway generation for inhalation of 400 μg albuterol using a metered dose inhaler (MDI) and a dry powder inhaler (DPI). The deposited dose was input into the PBPK model at the epithelial lining fluid (ELF) to predict drug concentrations in plasma and tissues. The PD model predicted ΔFEV₁ at the subepithelium effect-site. Two key observations emerged. First, MDI use shifted the inhaled dose toward the respiratory region, whereas DPI delivery resulted in greater deposition in generations G2-G5. Second, asthmatic subjects exhibited higher resistance than healthy subjects in generations G5-G10, resulting in larger ΔFEV₁. Fitted PD model parameters, yielded a half-maximal effective concentration (EC₅₀ =1.1±0.07 nM) and Hill slope (n =1.6±0.1), with R² = 0.98. In summary, we developed a subject-specific coupled CFPD-PBPK/PD framework to predict inhaled albuterol exposure in plasma and ELF, as well as the resulting ΔFEV₁ clinical response. This approach has the potential to inform inhaler selection, optimize dosing strategies, and enable disease-specific inhalational therapies. The present study was limited to male subjects, and future work should extend the framework to include sex-specific physiological differences.