Department of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, TX, USA.
Department of Computer Science, The University of Texas at Austin, TX, USA.
Int J Pharm. 2022 Oct 15;626:122179. doi: 10.1016/j.ijpharm.2022.122179. Epub 2022 Sep 7.
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.
干粉吸入器（DPIs）是治疗呼吸系统疾病最广泛使用的装置之一。无薄膜冷冻（TFF）是一种颗粒工程技术，已被证明可制备具有增强物理化学性质的吸入干粉。气溶胶性能（由细颗粒分数（FPF）和质量中值空气动力学直径（MMAD）表示）是产品开发过程中的一个重要考虑因素。然而，常规的配方开发方法需要进行许多反复试验，既费力又费时。作为一种先进的技术，机器学习在药物科学中得到了更多的关注，并已广泛应用于不同的环境中。在这项研究中，我们成功地使用表格数据和扫描电子显微镜（SEM）图像构建了气溶胶性能的预测模型。TFF 技术用于制备 134 种干粉制剂，这些制剂被收集为表格数据集。在测试了许多机器学习模型之后，我们确定随机森林（RF）模型最适合 FPF 预测，平均绝对误差为±7.251%，而人工神经网络（ANN）在估计 MMAD 方面表现最佳，平均绝对误差为±0.393μm。此外，还使用卷积神经网络对 SEM 图像进行分类，并证明了其在预测三种不同药物制剂的 316 张 SEM 图像方面具有很高的准确性（>83.86%）和适应性。总之，成功建立了使用表格数据和图像分类的机器学习模型，以评估吸入干粉的气溶胶性能。这些机器学习模型有助于 TFF 技术制造的干粉吸入剂的产品开发过程，并有可能显著减少产品开发工作量。机器学习方法将来也可以应用于其他配方设计和开发过程。
