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Latent Structure Analysis of Wet-Granulation Tableting Process Based on Structural Equation Modeling.

Hiroki Katayama1, Shoko Itakura1, Hiroaki Todo1

  • 1Department of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Josai University.

Chemical & Pharmaceutical Bulletin
|July 1, 2021
PubMed
Summary

Quality by Design (QbD) implementation in pharmaceutical manufacturing is complex. Structural Equation Modeling (SEM) with Bayesian estimation offers a way to understand causal relationships between process parameters, material attributes, and quality attributes for better QbD.

Keywords:
Bayesian estimationMarkov chain Monte Carlo simulationquality by designstructural equation modelingwet-granulation tableting

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Chemistry
  • Quality by Design (QbD)

Background:

  • Implementing Quality by Design (QbD) in pharmaceutical manufacturing presents challenges due to process complexity.
  • A deep understanding of the science underpinning manufacturing is crucial for effective QbD.

Purpose of the Study:

  • To explore the application of Structural Equation Modeling (SEM) for understanding causal relationships in pharmaceutical manufacturing processes.
  • To identify key variables and latent factors influencing product quality within a QbD framework.

Main Methods:

  • Structural Equation Modeling (SEM) was utilized to analyze causal relationships between process parameters, material attributes, and quality attributes.
  • Bayesian estimation with Markov chain Monte Carlo (MCMC) simulation was employed to address challenges in model fitting.
  • A wet-granulation tableting process for acetaminophen served as the model manufacturing case study.

Main Results:

  • A model was identified, illustrating causal links between process parameters, material attributes, and quality attributes.
  • The study demonstrated the feasibility of using SEM to model these relationships even without observational data.
  • Bayesian estimation effectively handled difficulties in fitting the proposed SEM model to data.

Conclusions:

  • Structural Equation Modeling (SEM) is a valuable tool for implementing Quality by Design (QbD) in pharmaceutical manufacturing.
  • This approach aids in elucidating complex causal relationships essential for process understanding and control.
  • The methodology provides a robust framework for enhancing pharmaceutical product quality through QbD principles.