Bluepharma - Indústria Farmacêutica S.A., São Martinho do Bispo, 3045-016 Coimbra, Portugal; Faculty of Pharmacy, University of Coimbra, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal.
Bluepharma - Indústria Farmacêutica S.A., São Martinho do Bispo, 3045-016 Coimbra, Portugal.
Eur J Pharm Biopharm. 2020 Jul;152:282-295. doi: 10.1016/j.ejpb.2020.05.012. Epub 2020 May 19.
Quality-by-Design (QbD) is a methodology used to build quality into products and is characterized by a well-defined roadmap. In this study, the application of Artificial Neural Networks (ANNs) in the QbD-based development of a test drug product is presented, where material specifications are defined and correlated with its performance in vivo. Along with other process parameters, drug particle size distribution (PSD) was identified as a critical material attribute and a three-tier specification was needed. An ANN was built with only five hidden nodes in one hidden layer, using hyperbolic tangent functions, and was validated using a random holdback of 33% of the dataset. The model led to significant and valid prediction formulas for the three responses, with R values higher than 0.94 for all responses, both for the training and the validation datasets. The prediction formulas were applied to contour plots and tight limits were set based on the design space and feasible working area for the drug PSD, as well as for process parameters. The manufacturing process was validated through the production of three exhibit batches of 180,000 tablets in the industrial GMP facility, and the ANN model was applied to successfully predict the in vitro dissolution, with a bias of approximately 5%. The product was then tested on two clinical studies (under fasting and fed conditions) and the criteria to demonstrate bioequivalence to the Reference Listed Drug were met. In this study, ANNs were successfully applied to support the establishment of drug specifications and limits for process parameters, bridging the formulation development with in vitro performance and the positive clinical results obtained in the bioequivalence studies.
质量源于设计（QbD）是一种将质量构建到产品中的方法，其特点是有明确的路线图。在本研究中，介绍了人工神经网络（ANNs）在基于 QbD 的试验药物产品开发中的应用，其中定义了材料规格并将其与体内性能相关联。除了其他工艺参数外，药物粒径分布（PSD）被确定为关键的材料属性，需要三级规格。ANN 仅使用一个隐藏层中的五个隐藏节点构建，使用双曲正切函数，并使用数据集的 33%随机保留进行验证。该模型为三个响应建立了显著且有效的预测公式，所有响应的 R 值均高于 0.94，无论是训练数据集还是验证数据集。预测公式应用于等高线图，并根据药物 PSD 的设计空间和可行工作区以及工艺参数设置严格的限制。通过在工业 GMP 设施中生产三批 18 万片的展示批次来验证制造工艺，并且成功应用 ANN 模型来预测体外溶出度，偏差约为 5%。然后，该产品在两项临床研究（空腹和进食条件下）中进行了测试，并达到了与参比药物生物等效性的标准。在本研究中，成功应用了 ANNs 来支持药物规格和工艺参数限制的确立，将配方开发与体外性能和生物等效性研究中获得的积极临床结果联系起来。
