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Simulation-based content uniformity engineering for drug development and manufacturing.

Kevin T Chu1, Remus Osan2, Nicole Tin2

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Summary
This summary is machine-generated.

Stochastic models and Monte Carlo simulations assess content uniformity (CU) risk. This study provides tools for process chemists to engineer particle size distributions (PSD) and manage CU risk in pharmaceutical manufacturing.

Keywords:
Content UniformityMonte Carlo SimulationParticle Size Distribution

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Process Engineering

Background:

  • Stochastic models and Monte Carlo (MC) simulations are crucial for evaluating content uniformity (CU) risk in pharmaceutical manufacturing, reducing the need for extensive experimentation.
  • The accuracy and appropriate application of these simulations are paramount for their utility within operational workflows.

Purpose of the Study:

  • To analyze a stochastic model of tablet formation for quantitative characterizations of dose regimes based on active pharmaceutical ingredient (API) particle size distribution (PSD), offering early CU risk guidance.
  • To validate the significance of upper particle size cutoff diameters in MC simulations, considering manufacturing processes and safety margins.
  • To demonstrate simulation-based tools for PSD engineering and CU risk assessment, integrating into common pharmaceutical development workflows.

Main Methods:

  • Analysis of a stochastic tablet formation model to derive quantitative dose regime characterizations linked to API PSD.
  • Monte Carlo (MC) simulation of the stochastic tablet formation model to assess the impact of particle size cutoffs.
  • Development of simulation-based tools for process chemists and formulators for PSD engineering and CU risk assessment.

Main Results:

  • Quantitative characterizations of dose regimes based on API PSD were developed, providing early CU risk indicators.
  • The importance of selecting appropriate upper particle size cutoff diameters in MC simulations, incorporating manufacturing process considerations and safety margins, was validated.
  • Simulation-based tools were demonstrated for early-stage PSD target guidance, prediction of United States Pharmacopeia (USP) <905> pass rates, and identification of low-risk API PSD parameters and dose strengths.

Conclusions:

  • Stochastic modeling and MC simulations offer powerful, validated tools for assessing and managing CU risk in pharmaceutical development.
  • Simulation-based approaches enable proactive PSD engineering and risk assessment, optimizing pharmaceutical manufacturing processes.
  • The developed tools facilitate informed decision-making for process chemists and formulators, enhancing the efficiency and reliability of drug product development.