University of Hertfordshire

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Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks

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Documents

  • Joanna Muddle
  • Stewart B. Kirton
  • Irene Parisini
  • Andrew Muddle
  • Darragh Murnane
  • Jogoth Ali
  • Marc Brown
  • Clive Page
  • Ben Forbes
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Original languageEnglish
Pages (from-to)313-321
Number of pages9
JournalJournal of Pharmaceutical Sciences
Volume106
Issue1
Early online date9 Nov 2016
DOIs
Publication statusPublished - 1 Jan 2017

Abstract

Dry powder inhalers are increasingly popular for delivering drugs to the lungs for the treatment of respiratory diseases, but are complex products with multivariate performance determinants. Heuristic product development guided by in vitro aerosol performance testing is a costly and time-consuming process. This study investigated the feasibility of using artificial neural networks (ANNs) to predict fine particle fraction (FPF) based on formulation device variables. Thirty-one ANN architectures were evaluated for their ability to predict experimentally determined FPF for a self-consistent dataset containing salmeterol xinafoate and salbutamol sulfate dry powder inhalers (237 experimental observations). Principal component analysis was used to identify inputs that significantly affected FPF. Orthogonal arrays (OAs) were used to design ANN architectures, optimized using the Taguchi method. The primary OA ANN r2 values ranged between 0.46 and 0.90 and the secondary OA increased the r2 values (0.53-0.93). The optimum ANN (9-4-1 architecture, average r2 0.92 ± 0.02) included active pharmaceutical ingredient, formulation, and device inputs identified by principal component analysis, which reflected the recognized importance and interdependency of these factors for orally inhaled product performance. The Taguchi method was effective at identifying successful architecture with the potential for development as a useful generic inhaler ANN model, although this would require much larger datasets and more variable inputs.

Notes

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Pharmaceutical Sciences after peer review and technical editing by the publisher. Under embargo. Embargo end date: 9 November 2017. The version of record, Joanna Muddle, Stewart B. Kirton, Irene Parisini, Andrew Muddle, Darragh Murnane, Jogoth Ali, Marc Brown, Clive Page and Ben Forbes, ‘Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks’, Journal of Pharmaceutical Sciences, Vol 106(1): 313-321, first published online on 9 November 2016, is available online via doi: http://dx.doi.org/10.1016/j.xphs.2016.10.002 0022-3549/© 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

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