In-Silico Lung Modeling Platform for Inhaled Drug Delivery

Antonio Cabal, Guido Jajamovich, Khamir Mehta, Peng Guo, Andrzej Przekwas

The inability to measure local lung concentrations responsible for lung efficacy is the main challenge common to any inhalation drug delivery program targeting the lungs. The model described in this work is a multiscale mechanism-based integrated computational platform (lung platform) to provide mechanistic insights into key physiological elements associated with pulmonary drug delivery: deposition, mucociliary clearance, dissolution, absorption, transport, distribution, partition, and action. Two versions of the lung platform (LP) were developed for translational purposes, one for rats (RLP) and one for humans (HLP) to account for the species specific physiological based differences in airway morphology and drug distribution throughout the body. All these components facilitate a prediction of regional distribution of drug within the lungs.
Published data for two inhaled corticosteroids (mometasone furoate, budesonide), a short-acting beta-agonist (salbutamol), and a long-acting beta-agonist (formoterol) in both rats and humans were used to qualify the model and illustrate its prediction ability. The translational pharmacokinetic benefits of the lung platform are illustrated using the selected compounds, where the physicochemical properties and the drug delivery details constitute the main input to the model. Clearance was the only parameter adjusted using weight based allometric scaling to translate from rats to humans. The results show the feasibility of the proposed modeling approach, once it has been properly qualified using existing data, toward bridging the gap between inhaled data in preclinical species and the prediction of human lung and systemic exposure.

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