2026-10-07T04:58:29Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33737320 2026-09-14T12:43:38Z category_24862 item_type_3 month_year_09_2026

<b>Integration of Computational Fluid Dynamics and Physiologically Based Pharmacokinetic Modeling to Predict Human Pulmonary Absorption and Systemic Exposure of Inhaled Drugs</b> Seungchan Kim (292997) Eunsuk Yang (22353097) Jang-Won Lee (312197) Suk-Jae Chung (5832092) Yoo-Seong Jeong (5832080) Pharmaceutical sciences Pulmonary drug delivery Computational fluid dynamics Physiologically based pharmacokinetics Particle deposition In vitro/in silico-based prediction <p dir="ltr">Mechanistic prediction of pulmonary absorption and systemic pharmacokinetics (PK) for inhaled drugs remains a key challenge that is not adequately addressed by existing physiologically based pharmacokinetic (PBPK) models. We developed an integrated computational fluid dynamics (CFD)-PBPK framework that links generation-resolved particle deposition with mechanistic pulmonary absorption to predict human PK using <i>in vitro</i> and<i> </i><i>in silico</i> inputs. Airflow-driven particle deposition across airway generations 0-23 was characterized using a two-dimensional CFD model, wherein a particle size-dependent deposition velocity () was introduced and calculated based on particle diameter. Generation-specific deposited doses estimated from aerosol properties provided the initial conditions for subsequent PBPK simulations. Dissolution, epithelial permeation, and mucociliary clearance were parameterized from commonly measured<i> </i><i>in vitro</i>/<i>in silico</i> data, while systemic disposition was described by a minimal PBPK model fitted to intravenous PK data. When evaluated using 44 inhalation datasets for 15 drugs curated from the literature, the model reasonably predicted the extent and rate of absorption within two-fold of observations in 39 and 33 datasets. Sensitivity analyses confirmed that model predictions were responsive to deposition, dissolution, and permeability parameters. This framework translates aerosol characteristics into systemic PK outcomes, using commonly measured <i>in vitro</i>, <i>in silico</i>, and intravenous PK data.</p> 2026-09-14T12:43:38Z Dataset Dataset 10.6084/m9.figshare.33737320.v1 https://figshare.com/articles/dataset/_b_Integration_of_Computational_Fluid_Dynamics_and_Physiologically_Based_Pharmacokinetic_Modeling_to_Predict_Human_Pulmonary_Absorption_and_Systemic_Exposure_of_Inhaled_Drugs_b_/33737320 CC BY 4.0