P131 MODELLING THE IMPACT OF INHALED DRUG PARTICLE SIZE OF SALBUTAMOL ON BRONCHODILATOR RESPONSE USING ARTIFICIAL INTELLIGENCE

¹M. de Matas, ¹Q. Shao, ²M. Biddiscombe, ³H. Chrystyn, ²P. J. Barnes, ¹O. S. Usmani. ¹Institute of Pharmaceutical Innovation, University of Bradford, Bradford, UK, ²Imperial College London and Royal Brompton Hospital, London, UK, ³School of Pharmacy, University of Huddersfield, Huddersfield, UK

Introduction We have previously shown that artificial neural networks (ANN) can be used to generate in vitro-in vivo correlation (IVIVC) models to predict the bronchodilator response (BR) to inhaled salbutamol (SB) delivered by polydisperse aerosols from dry powder inhalers and nebulisers in asthmatic subjects.1 BR was highly dependent on aerodynamic drug particle size (APS).

Objectives ANN software was therefore used in this study to model in vitro and in vivo data from 18 mild-moderate asthmatics receiving monodisperse (uniform drug particle sizes) SB aerosols of 1.5, 3 and 6 μm MMAD in a cumulative dosing schedule of 10, 20, 40 and 100 μg.2 The intention of this strategy was to explore the impact of APS on BR using accurate clinical aerosol science data and construct a model able to predict in vivo clinical outcomes in individual patients.

Methods Input variables to the model were APS, body surface area (BSA), age, pre-treatment forced expiratory volume in 1 s (FEV₁)%predicted, forced vital capacity (FVC), cumulative emitted drug dose and bronchodilator reversibility to SB 200 μg metered dose …