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dc.contributor.authorMuddle, Joanna
dc.contributor.authorKirton, Stewart B.
dc.contributor.authorParisini, Irene
dc.contributor.authorMuddle, Andrew
dc.contributor.authorMurnane, Darragh
dc.contributor.authorAli, Jogoth
dc.contributor.authorBrown, Marc
dc.contributor.authorPage, Clive
dc.contributor.authorForbes, Ben
dc.date.accessioned2017-02-07T18:25:54Z
dc.date.available2017-02-07T18:25:54Z
dc.date.issued2017-01-01
dc.identifier.citationMuddle , J , Kirton , S B , Parisini , I , Muddle , A , Murnane , D , Ali , J , Brown , M , Page , C & Forbes , B 2017 , ' Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks ' , Journal of Pharmaceutical Sciences , vol. 106 , no. 1 , pp. 313-321 . https://doi.org/10.1016/j.xphs.2016.10.002
dc.identifier.issn0022-3549
dc.identifier.urihttp://hdl.handle.net/2299/17605
dc.descriptionThis 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.
dc.description.abstractDry 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.en
dc.format.extent9
dc.format.extent933293
dc.language.isoeng
dc.relation.ispartofJournal of Pharmaceutical Sciences
dc.subjectartificial neural networks
dc.subjectdry powder inhaler
dc.subjectfine particle fraction
dc.subjectin silico modeling
dc.subjectin vitro performance
dc.subjectnext-generation impactor
dc.subjectMedicine(all)
dc.subjectPharmaceutical Science
dc.titlePredicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networksen
dc.contributor.institutionDepartment of Pharmacy
dc.contributor.institutionUniversity of Hertfordshire
dc.contributor.institutionHealth & Human Sciences Research Institute
dc.contributor.institutionSchool of Life and Medical Sciences
dc.contributor.institutionDepartment of Pharmacy, Pharmacology and Postgraduate Medicine
dc.contributor.institutionCentre for Research into Topical Drug Delivery and Toxicology
dc.contributor.institutionPharmaceutics
dc.contributor.institutionAirway Group
dc.contributor.institutionPharmaceutical Analysis and Product Characterisation
dc.description.statusPeer reviewed
dc.date.embargoedUntil2017-11-09
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85002674046&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1016/j.xphs.2016.10.002
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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