Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome.

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.