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.
Gedeon Richter Plc., Formulation R&D, Gyömrői u. 19-21, H-1103 Budapest, Hungary.
Int J Pharm. 2023 Feb 25;633:122620. doi: 10.1016/j.ijpharm.2023.122620. Epub 2023 Jan 18.
As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANN-based control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality.
随着制药行业越来越多地采用 Pharma 4.0 概念，人们越来越需要根据制造或过程数据有效地预测产品质量。虽然人工神经网络 (ANN) 已成为数据丰富环境中的强大工具，但由于其黑盒性质，它们在制药制造中的实施受到阻碍。在这项工作中，开发和解释了 ANN，以通过对开发或制造数据的回顾性分析来展示它们通过增加对工艺的理解的适用性。通过来自中试开发的制造和光谱数据来预测缓释、直接压片的体外溶出度和硬度。与使用 NIR 或拉曼光谱作为预测因子相比，使用材料属性和操作参数的 ANN 提供了更好的结果。通过敏感性分析对 ANN 进行解释，有助于确定批间变异性的根本原因，例如羟丙基甲基纤维素赋形剂的粒径、等级或替代的变异性。还成功地利用基于 ANN 的控制策略通过灵活操作压片工艺来减轻批间变异性。所提出的方法可以适应从活性物质合成到配方的任意数据丰富的制造步骤，以从制造或开发数据预测质量，并获得对工艺的理解和一致的产品质量。
