Gams Matjaž, Horvat Matej, Ožek Matej, Luštrek Mitja, Gradišek Anton
Department of Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia.
AAPS PharmSciTech. 2014 Dec;15(6):1447-53. doi: 10.1208/s12249-014-0174-z. Epub 2014 Jun 27.
We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.
我们开发了一种基于机器学习的新方法，以根据过程分析技术（PAT）和质量源于设计（QbD）倡议，促进药品（如片剂）的制造过程。我们的方法将先前生产运行中可用的数据与机器学习算法相结合，这些算法由具有生产过程专业知识的人工操作员辅助。过程参数包括与前体原材料属性相关的参数以及与制造过程本身相关的参数。在制造过程中，我们的方法允许生产操作员在其已证明的可接受范围内检查各种过程参数设置的影响，以便在实际批量生产之前选择最有前景的值。人工操作员与人工智能系统之间的交互提高了性能和质量。我们成功地将该方法应用于一家制药公司提供的关于一种正在开发的特定产品（片剂）的数据。我们与其他一些机器学习方法相比，测试了该方法的准确性。该方法特别适用于分析以数据量有限为特征的制造过程。
