Deep learning for continuous manufacturing of pharmaceutical solid dosage form

• Published in: European journal of pharmaceutics and biopharmaceutics 2020-08, Vol.153, p.95-105
• Main Authors: Roggo, Yves, Jelsch, Morgane, Heger, Philipp, Ensslin, Simon, Krumme, Markus
• Format: Article
• Language: English
• Subjects: Chemistry, Pharmaceutical - methods; Continuous manufacturing; Deep Learning; Drug Compounding - methods; Drug Industry - methods; Excipients - chemistry; Pharmaceutical Preparations - chemistry; Process analytical technology; Process data analytics; Process data science; Process monitoring; Solid dosage form; Tablets - chemistry; Temperature
• ISSN: 0939-6411; 1873-3441
• DOI: 10.1016/j.ejpb.2020.06.002

[Display omitted] •Improved process understanding of continuous wet granulation process with deep learning technology.•Application of deep learning techniques for an innovative process monitoring.•Optimization of deep neural networks for pharmaceutical process monitoring.•Application of innovative Process Analytical Technology (PAT) equipment.•Real-world example of process monitoring of a continuous production line. Continuous Manufacturing (CM) of pharmaceutical drug products is a new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid dosage forms was investigated. The line was composed of the subsequent continuous unit: operations feeding – twin-screw wet-granulation – fluid-bed drying – sieving and tableting. The formulation of a commercial entity was selected for this study. Several critical process parameters were evaluated in order to probe the process and to characterize the impact on quality attributes. Seven critical process parameters have been selected after a risk analysis: API and excipient mass flows of the two feeders, liquid feed rate and rotation speed of the extruder and rotation speed, temperature and airflow of the dryer. Eight quality attributes were controlled in real time by Process Analytical Technologies (PAT): API content after blender, after dryer, in tablet press feed frame and of tablet, LOD after dryer and PSD after dryer (three PSD parameters: x10 x50 x90). The process parameter values were changed during production in order to detect the impact on the quality of the final product. The deep learning techniques have been used in order to predict the quality attribute (output) with the process parameters (input). The use of deep learning reduces the noise and simplify the data interpretation for a better process understanding. After optimization, three hidden layers neural network were selected with 6 hidden neurons. The activation function ReLU (Rectified Linear Unit) and the ADAM optimizer were used with 2500 epochs (number of learning cycle). API contents, PSD values and LOD values were estimated with an error of calibration lower than 10%. The level of error allow an adequate process monitoring by DNN and we have proven that the main critical process parameters can be identified at a higher levelof process understanding. The synergy between PAT and process data science creates a superior monitoring framework of the continuous manufacturing line