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. 2022 Oct 15:626:122179.
doi: 10.1016/j.ijpharm.2022.122179. Epub 2022 Sep 7.

The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology

Junhuang Jiang  1 , Han-Hsuan Peng  1 , Zhenpei Yang  2 , Xiangyu Ma  3 , Sawittree Sahakijpijarn  4 , Chaeho Moon  1 , Defang Ouyang  5 , Robert O Williams Iii  6
Affiliations

The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology

Junhuang Jiang et al. Int J Pharm. .

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.

Keywords: Aerosol performance; Deep learning; Dry powder inhaler (DPI); Image analysis; Machine learning; Thin-film-freezing.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [ROW reports financial support by TFF Pharmaceuticals, Inc. ROW reports a relationship with TFF Pharmaceuticals, Inc. that includes consulting or advisory, equity or stocks, and funding grants. CM reports a relationship with TFF Pharmaceuticals, Inc. that includes a consulting or advisory capacity. The terms of conflicts of interest have been reviewed and approved by the University of Texas at Austin in accordance with its institutional policy on objectivity in research.].

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