A mechanism-based computational toolkit to optimize age-specific pediatric pulmonary drug delivery

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A mechanism-based computational toolkit to optimize age-specific pediatric pulmonary drug delivery Project Summary/Abstract: The selection of the most age-appropriate combination of drug, device, and interface is critical for the effective administration of any prescribed therapy. This is especially relevant in pediatric cases of respiratory disorders that require inhalation therapy. However, in spite of all the modern advancements in inhalation therapies, the fraction of drugs reaching the lungs for maximal therapeutic effects remains low while increasing the dose causes an increase in systemic concentration and subsequent toxicity. The lack of approved age-appropriate drug/device combinations has been limited by experimental constraints in children and the plasticity of pediatric airway structures that continuously evolves from birth to adulthood, influencing airflow dynamics and respiratory mechanics. Motivated by such shortcomings, which cannot be solved by experimental strategies alone, we propose to develop a novel multiscale computational toolkit to simulate deposition, dissolution, absorption, transport, clearance, and actions of inhaled drug products. The core of this toolkit will be an integral framework of computational fluid dynamics (CFD) and whole-body physiology-based pharmacokinetic (PBPK) models, specific to selected age groups. In Phase I, we will (1) develop image-based and anatomically-faithful 3D CFD models of pediatric airway geometry to calculate age-, drug-, and device-specific deposition patterns; (2) extend the CFD models to account for various physiological and pathological settings, such as constriction of airway geometry; and (3) integrate CFD deposition models with PBPK models to estimate systemic concentration of the inhaled drugs. This workflow, in collaboration with Department of Pediatrics, Pulmonology Division at the University of Arkansas College of Medicine, and Department of Radiology at Duke University, will primarily focus on modeling two test subjects for corticosteroid inhalation using two commonly used pediatric inhalation devices. In Phase II, we will further improve our computational tools by model validation on additional pediatric age-groups, drugs and drug combinations, and other pediatric inhalation devices, such as, nebulizers and soft mist inhalers. Our goal of using these computational strategies is to develop a product that will aim to facilitate drug development by identifying key biopharmaceutical factors affecting efficacy and safety of inhaled drugs that help guide age-appropriate dosing, device, and interface selection to better inform clinical practice. The final deliverable will be a commercial quality software package with graphical and instant response abilities to estimate regional and global deposition of inhaled drugs in the human respiratory tract and their fate through its appearance in the systemic blood until eliminated from the body. The software with pre-loaded test cases will be made available at no cost to NIH/FDA researchers for evaluation and testing.