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Risk Assessment for a Twin-Screw Granulation Process Using a Supervised Physics-Constrained Auto-encoder and Support

Chaitanya Sampat¹, Rohit Ramachandran²

¹Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, New Jersey, 08854, USA.

Pharmaceutical Research

|August 4, 2022

Summary

This study introduces a new framework for quantitative quality risk management in pharmaceutical manufacturing. It uses a physics-constrained autoencoder and machine learning to predict process outcomes and assess risks in granulation.

Keywords:

physics-constrained supervised auto-encoders quality risk management quality-by-Design (QbD)twin-screw granulation

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Area of Science:

Pharmaceutical Science
Chemical Engineering
Data Science

Background:

Quality risk management is crucial in the pharmaceutical industry, directly impacting product performance.
ICH Q9 guidelines promote risk management using approaches like quality-by-design, but quantitative models for risk assessment are challenging.
Accurate quantitative models are needed to predict quality changes due to process variations.

Purpose of the Study:

To develop a quantitative framework for assessing risk in twin screw wet granulation processes.
To enable accurate prediction of process outcomes and understanding of granule growth behavior.
To facilitate risk estimation based on classified granule growth regimes.

Main Methods:

A physics-constrained autoencoder system was developed, with outputs constrained by physics-based boundary conditions.
Latent variables from the autoencoder were utilized in a support vector machine classifier.
The framework was applied to a twin screw wet granulation process.

Main Results:

The framework achieved 86% accuracy in predicting process outcomes.
Granule growth regimes were classified with a true positive rate of 0.73.
The classification enabled the estimation of associated process risks.

Conclusions:

The developed framework provides a quantitative approach to quality risk management in pharmaceutical manufacturing.
This method enhances the ability to predict and manage risks associated with granulation processes.
The study demonstrates the potential of integrating physics-based models with machine learning for pharmaceutical quality control.