Optimisation of inhaled drug delivery in the asthmatic lung

Project description

Asthma is a complex, multifactorial chronic airway disease affecting ~300 million people worldwide. It is characterised by acute episodes of airway hyperresponsiveness (a rapid constriction of the bronchial airways to much lower doses of contractile agonist than in non-asthmatic subject) and bronchoconstriction. Treatment of these acute events often involves the inhalation of aerosolised pharmaceuticals, such as corticosteroids, however delivery of these therapies to the target airways is difficult to predict due to the complex dynamics of airway constriction. We have previously developed multi-scale mathematical models of airway smooth muscle (ASM) contraction which show that under certain conditions deep inspirations (DIs) can counteract this bronchoconstriction and allow airways to reopen, but that in others DIs may have a detrimental effect and enhance the constrictions [2,3]. The lung however consists of tens of thousands of airways, and the interactions between these airways within a branching network can lead to complex emergent phenomena [4] that make predicting outcomes of particular treatments or interventions for specific asthmatic individuals extremely difficult [5]. Thus, to better understand under what conditions deep inspirations, and subsequent delivery of inhaled treatment may have a beneficial effect, we propose to develop a computational model that incorporates the detailed active mechanical responses of each individual airway into a coupled model of the whole airway tree.

Working with Dr Carl Whitfield at the University of Manchester, we will:

• Use model-reduction techniques, such as Gaussian Process emulation, to parameterise existing models of ASM contraction and airway mechanics [1–3,8].
• Incorporate reduced models into existing organ-scale simulations of ventilation and gas transport on realistic airway networks [6,7] that currently do not account for non-linear mechanics of individual airways.
• Use these combined simulations to discover the key determinants of how deep inspirations affect bronchoconstriction at the organ-scale and thus enable optimisation of inhaled drug-delivery in bronchoconstricted airways.

In collaboration with Prof Chris Brightling (a respiratory clinician at the University of Leicester), there will be the opportunity to test and validate these models against existing breath measurement datasets (e.g. forced oscillometry, multiple breath washout, and/or measurements of exhaled volatile compounds) from patient cohorts in longitudinal studies.