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Deep learning for continuous manufacturing of pharmaceutical solid dosage form.

Yves Roggo1, Morgane Jelsch1, Philipp Heger1

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PubMed
Summary
This summary is machine-generated.

This study demonstrates how deep learning models and Process Analytical Technologies (PAT) can effectively monitor and understand continuous manufacturing (CM) of pharmaceutical products. The integrated approach accurately predicts quality attributes, enhancing process control and product knowledge.

Keywords:
Continuous manufacturingDeep learningProcess analytical technologyProcess data analyticsProcess data scienceProcess monitoringSolid dosage form

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Engineering
  • Data Science

Background:

  • Continuous Manufacturing (CM) offers a novel approach to pharmaceutical drug production.
  • Implementing Good Manufacturing Practice (GMP) compliant continuous wet granulation lines is crucial for solid dosage forms.
  • Understanding the impact of critical process parameters (CPPs) on quality attributes is essential for robust CM.

Purpose of the Study:

  • To investigate a GMP continuous wet granulation line for pharmaceutical solid dosage forms.
  • To evaluate the impact of critical process parameters on key quality attributes in real-time.
  • To apply deep learning techniques for enhanced process understanding and monitoring.

Main Methods:

  • A continuous wet granulation line involving feeding, twin-screw granulation, fluid-bed drying, sieving, and tableting was utilized.
  • Seven critical process parameters (e.g., mass flows, feed rates, speeds, temperature, airflow) were systematically varied.
  • Eight quality attributes (e.g., API content, LOD, PSD) were monitored in real-time using Process Analytical Technologies (PAT).
  • Deep learning models, specifically a neural network with ReLU activation and ADAM optimizer, were employed for quality prediction.

Main Results:

  • Deep learning models successfully predicted API content, PSD, and LOD with calibration errors below 10%.
  • The models facilitated the identification of critical process parameters influencing product quality.
  • A synergy between PAT and data science provided a superior monitoring framework for the CM line.

Conclusions:

  • Deep learning techniques significantly reduce noise and simplify data interpretation for improved process understanding in pharmaceutical CM.
  • The developed monitoring framework enables adequate process control and enhances knowledge of innovative continuous production lines.
  • The integration of PAT and data science is key to advancing the efficiency and reliability of pharmaceutical continuous manufacturing.