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Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
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Artificial intelligence in pharmaceutical manufacturing: Applications, case studies, and GxP implementation

Gowtham Nakka1, Sakshi Gupta1, Katti Kartik Reddy1

  • 1University of Louisiana at Lafayette, College of Engineering, 104 E University Ave, Lafayette, LA 70504, USA.

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Artificial intelligence (AI) and machine learning (ML) enhance pharmaceutical manufacturing with advanced process controls and real-time monitoring. Successful implementation requires integrating AI/ML with quality by design (QbD) and Good Manufacturing Practice (GMP) for regulated environments.

Keywords:
Artificial intelligence (AI)Machine learning (ML)Process validationQuality by design (QbD)Risk assessmentSoft-sensors

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Analytical Technology (PAT)
  • Quality by Design (QbD)

Background:

  • AI and ML are increasingly utilized in pharmaceutical manufacturing for improved process controls and monitoring.
  • Integration of PAT and QbD principles enables AI/ML applications like soft sensors and advanced control systems.
  • Understanding Good Manufacturing Practice (GMP) is crucial for successful implementation in regulated settings.

Purpose of the Study:

  • To map common AI use-case archetypes in pharmaceutical manufacturing.
  • To define data requirements, intended use, and validation needs for AI/ML applications.
  • To integrate regulatory considerations into the pharmaceutical quality system (PQS).

Main Methods:

  • Integration of peer-reviewed studies and regulatory documents.
  • Development of a mapping system for AI use-case archetypes.
  • Analysis of eight case studies in continuous manufacturing and bioprocessing.

Main Results:

  • AI/ML applications demonstrated for multivariate statistical process control, potency soft sensors, and in-process control.
  • Case studies included model predictive control, chromatography anomaly detection, and automated visual inspection.
  • Successful deployment necessitates robust data monitoring systems with complete traceability.

Conclusions:

  • AI/ML offers significant potential for optimizing pharmaceutical manufacturing processes.
  • Sustainable AI/ML implementation requires adherence to GMP and robust data management.
  • Regulatory compliance throughout the model lifecycle is essential for AI/ML integration into PQS.