6 research outputs found
Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset
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 ANNbased 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