title: Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks.
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 r2values ranged between 0.46 and 0.90 and the secondary OA increased the r2values (0.53-0.93). The optimum ANN (9-4-1 architecture, average r20.92 ± 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.
authors.affiliation: Institute of Pharmaceutical Science, King's College London, 150 Stamford Street, London SE19NH, UK; Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, 150 Stamford Street, London SE19NH, UK.
authors.forename: Joanna
authors.initials: J
authors.lastname: Muddle
authors.affiliation: Department of Pharmacy, University of Hertfordshire, Hatfield, Herts AL109AB, UK. Electronic address: s.b.kirton3@herts.ac.uk.
authors.forename: Stewart B
authors.initials: SB
authors.lastname: Kirton
authors.affiliation: Department of Pharmacy, University of Hertfordshire, Hatfield, Herts AL109AB, UK.
authors.forename: Irene
authors.initials: I
authors.lastname: Parisini
authors.affiliation: MedPharm Ltd, R&D Centre, Units 1 and 3 Chancellor Court, 50 Occam Road, Surrey Research Park, Guildford GU27AB, UK.
authors.forename: Andrew
authors.initials: A
authors.lastname: Muddle
authors.affiliation: Department of Pharmacy, University of Hertfordshire, Hatfield, Herts AL109AB, UK.
authors.forename: Darragh
authors.initials: D
authors.lastname: Murnane
authors.affiliation: Department of Pharmacy, University of Hertfordshire, Hatfield, Herts AL109AB, UK.
authors.forename: Jogoth
authors.initials: J
authors.lastname: Ali
authors.affiliation: Department of Pharmacy, University of Hertfordshire, Hatfield, Herts AL109AB, UK; MedPharm Ltd, R&D Centre, Units 1 and 3 Chancellor Court, 50 Occam Road, Surrey Research Park, Guildford GU27AB, UK.
authors.forename: Marc
authors.initials: M
authors.lastname: Brown
authors.affiliation: Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, 150 Stamford Street, London SE19NH, UK.
authors.forename: Clive
authors.initials: C
authors.lastname: Page
authors.affiliation: Institute of Pharmaceutical Science, King's College London, 150 Stamford Street, London SE19NH, UK.
authors.forename: Ben
authors.initials: B
authors.lastname: Forbes
mesh_terms: D000420:Albuterol / Q000008:administration & dosage* / Q000737:chemistry; D001993:Bronchodilator Agents / Q000008:administration & dosage* / Q000737:chemistry; D058995:Dry Powder Inhalers / Q000379:methods*; D016571:Neural Networks, Computer; D010316:Particle Size; D011208:Powders; D025341:Principal Component Analysis; D000068299:Salmeterol Xinafoate / Q000008:administration & dosage* / Q000737:chemistry
chemical_list: D001993:Bronchodilator Agents; D011208:Powders; D000068299:Salmeterol Xinafoate; D000420:Albuterol
keywords: artificial neural networks; dry powder inhaler; fine particle fraction; in silico modeling; in vitro performance; next-generation impactor