Honti Barbara, Farkas Attila, Nagy Zsombor Kristóf, Pataki Hajnalka, Nagy Brigitta
Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
Int J Pharm. 2024 Sep 5;662:124509. doi: 10.1016/j.ijpharm.2024.124509. Epub 2024 Jul 22.
Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept.
由于销售商品成本持续上涨，制药行业面临诸多挑战，而基于数据驱动决策的首次就正确原则对于维持竞争力变得更加紧迫。因此，在这项工作中，通过分析来自真实制药压片生产过程的开放获取数据集，开发、比较并解释了三种不同类型的人工神经网络（ANN）模型。首先，使用多层感知器（MLP）模型，根据20种原材料特性以及在整个压片过程中收集的时间序列数据的25个统计描述符（例如压片速度和压力）来描述总废品量。然后，除了原材料特性外，还使用10个过程时间序列数据，通过长短期记忆（LSTM）和双向LSTM（biLSTM）递归神经网络（RNN）预测制造过程中的累积废品量。LSTM网络用于预测废品产生情况，以便采取预防措施。结果表明，RNN能够预测废品轨迹，最佳模型在训练和测试时的均方根误差分别为1096片和2174片。为了更好地理解过程、模型，并帮助决策支持系统和控制策略，对所有ANN实施了解释方法，通过识别最具影响力的材料属性和过程参数，增强了对过程的理解。所提出的方法适用于制药学多个领域的各种关键质量属性，因此是实现制药4.0概念的有用工具。
