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

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.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.Peer reviewe

Interaction of Formulation and Device Factors Determine the In Vitro Performance of Salbutamol Sulphate Dry Powders for Inhalation

Joanna Muddle, Darragh Murnane, Irene Parisini, Marc Brown, Clive Page, and Ben Forbes, 'Interaction of Formulation and Device Factors Determine In Vitro Performance of Salbutamol Sulphate Dry Powders for Ihnalation', Journal of Pharmaceutical Sciences, Vol. 104 (11): 3861-3869, November 2015, doi: https://doi.org/10.1002/jps.24599. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.A variety of capsule-based dry powder inhalers were used to evaluate formulation-device interaction. The in vitro deposition of salbutamol sulphate (SS) was compared directly to published data for salmeterol xinafoate (SX). A 3(2) factorial design was used to assess the effect of SS formulations with three blends of different grade coarse lactose supplemented with different levels of fine lactose. These formulations were tested for homogeneity and evaluated for their in vitro deposition using Aeroliser, Handihaler and Rotahaler devices. The performance of the SS-lactose formulations differed across the grade of lactose and amount of fine lactose used compared to the same powder compositions blended with SX. SX had a greater fine particle fraction than SS for most of the comparable formulations, probably because of the different cohesiveness of the drugs. A head-to-head comparison of 'matched' SX and SS formulations when aerosolised from the same three devices demonstrated that formulation-device interactions are as critical in determining the in vitro deposition of drug-lactose blends as the identity of the active pharmaceutical ingredient. This work has revealed the limitations of the interpretative value of published in vitro performance data generated with a single device (even at equivalent aerosolisation force), when designing formulations for a different device.Peer reviewe