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The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology
Jiang, Junhuang1; Peng, Han Hsuan1; Yang, Zhenpei2; Ma, Xiangyu3; Sahakijpijarn, Sawittree4; Moon, Chaeho1; Ouyang, Defang5; Williams, Robert O.1
2022-10-15
Source PublicationInternational Journal of Pharmaceutics
ISSN0378-5173
Volume626
Abstract

Dry powder inhalers (DPIs) are one of the most widely used devices for treating respiratory diseases. Thin–film–freezing (TFF) is a particle engineering technology that has been demonstrated to prepare dry powder for inhalation with enhanced physicochemical properties. Aerosol performance, which is indicated by fine particle fraction (FPF) and mass median aerodynamic diameter (MMAD), is an important consideration during the product development process. However, the conventional approach for formulation development requires many trial-and-error experiments, which is both laborious and time consuming. As a state-of-the art technique, machine learning has gained more attention in pharmaceutical science and has been widely applied in different settings. In this study, we have successfully built a prediction model for aerosol performance by using both tabular data and scanning electron microscopy (SEM) images. TFF technology was used to prepare 134 dry powder formulations which were collected as a tabular dataset. After testing many machine learning models, we determined that the Random Forest (RF) model was best for FPF prediction with a mean absolute error of ± 7.251%, and artificial neural networks (ANNs) performed the best in estimating MMAD with a mean absolute error of ± 0.393 μm. In addition, a convolutional neural network was employed for SEM image classification and has demonstrated high accuracy (>83.86%) and adaptability in predicting 316 SEM images of three different drug formulations. In conclusion, the machine learning models using both tabular data and image classification were successfully established to evaluate the aerosol performance of dry powder for inhalation. These machine learning models facilitate the product development process of dry powder for inhalation manufactured by TFF technology and have the potential to significantly reduce the product development workload. The machine learning methodology can also be applied to other formulation design and development processes in the future.

KeywordAerosol Performance Deep Learning Dry Powder Inhaler (Dpi) Image Analysis Machine Learning Thin-film-freezing
DOI10.1016/j.ijpharm.2022.122179
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaPharmacology & Pharmacy
WOS SubjectPharmacology & Pharmacy
WOS IDWOS:000859345400003
Scopus ID2-s2.0-85137615492
Fulltext Access
FWCI3.6942668
Citation statistics
Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionInstitute of Chinese Medical Sciences
Affiliation1.Department of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, United States
2.Department of Computer Science, The University of Texas at Austin, United States
3.Global Investment Research, Goldman Sachs, United States
4.TFF Pharmaceuticals, Inc., United States
5.State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
Recommended Citation
GB/T 7714
Jiang, Junhuang,Peng, Han Hsuan,Yang, Zhenpei,et al. The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology[J]. International Journal of Pharmaceutics,2022,626.
APA Jiang, Junhuang,Peng, Han Hsuan,Yang, Zhenpei,Ma, Xiangyu,Sahakijpijarn, Sawittree,Moon, Chaeho,Ouyang, Defang,&Williams, Robert O..(2022).The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology.International Journal of Pharmaceutics,626.
MLA Jiang, Junhuang,et al."The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology".International Journal of Pharmaceutics 626(2022).
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