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Deep learning for continuous manufacturing of pharmaceutical solid dosage form
یادگیری عمیق برای تولید مداوم فرم دوز جامد دارویی-2020 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 and
increase the knowledge of this innovative production line and the products that it makes. Keywords: Continuous manufacturing | Solid dosage form | Process monitoring | Process analytical technology | Deep learning | Process data science | Process data analytics |
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