ArticleClinical pharmacokinetics2025
Spatial Pharmacokinetic and Pharmacodynamic Modeling in Airway Mucus.
Article in Clinical pharmacokinetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
BACKGROUND AND
objectivesDiseases such as cystic fibrosis (CF) and non-CF bronchiectasis can cause extensive mucus formation in the lung, which may affect drug distribution and effects. As such, quantitative understanding of drug distribution in mucus may guide treatment optimization. Here, we aimed to develop a modeling framework to evaluate spatial distribution of drugs in mucus with CF as a proof of concept. In a case study, we demonstrated how spatial PK models can be used to predict spatial antimicrobial pharmacodynamics (PD).
methodsA spatial pharmacokinetic (PK) model in mucus was developed using discretized partial differential equations. Hypothetical drugs with realistic ranges for molecule/particle size (radius, r), mucin binding affinity, and half-lives were used to evaluate the impact of drug-specific factors on spatial distribution in mucus. Mucin concentration and muco-ciliary clearance were evaluated as biological system-specific factors. We then demonstrated how the spatial PK model can be used to predict antimicrobial drug effects of imipenem against the pathogen Pseudomonas aeruginosa in mucus.
resultsUnder intravenous PK profiles, molecular/particle size (r) was found to play a dominant role in mucus drug diffusion, while drug-mucin interactions and muco-ciliary clearance showed a minor impact. Small molecule drugs (r <1 nm) could readily penetrate mucus, whereas large molecules or particles (r >20 nm) showed differential spatial drug distribution. Our case study demonstrates that baseline spatial bacterial organization can impact the treatment outcome of imipenem against mucus-associated infections.
conclusionThe developed spatial PK modeling framework enabled quantitative description of the spatial distribution of drugs in airway mucus and can be of relevance to guide optimization of treatment strategies.
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