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A hybrid NIR-soft sensor method for real time in-process control during continuous direct compression manufacturing

Giuseppe Cogoni¹, Yang Angela Liu¹, Anas Husain²

¹Worldwide Research and Development, Pfizer Inc., Groton, CT 06340, USA.

International Journal of Pharmaceutics

|April 23, 2021

Summary

This summary is machine-generated.

This study introduces a hybrid method for continuous manufacturing, combining Near Infrared (NIR) spectroscopy with a potency soft sensor. This approach ensures robust online potency prediction, reducing model maintenance and improving method transfer.

Keywords:

Continuous pharmaceutical manufacturing Hybrid modeling In-process control NIR PAT Soft sensor

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Area of Science:

Pharmaceutical Manufacturing
Chemometrics
Process Analytical Technology (PAT)

Background:

Near Infrared (NIR) spectroscopy is a key Process Analytical Technology (PAT) tool for continuous manufacturing.
Monitoring blend potency and homogeneity in direct compression is crucial for quality control.
Traditional data-driven models require extensive data and frequent updates due to process variability.

Purpose of the Study:

To develop a robust method for online potency prediction in continuous direct compression manufacturing.
To minimize model maintenance and downtime associated with NIR spectroscopy.
To facilitate reliable cross-site method transfer for potency monitoring.

Main Methods:

Integration of an NIR PAT probe into the tablet press feed frame and diversion control system.
Quantification of NIR spectra using Partial Least-Squares (PLS) modeling, calibrated with offline tablet analysis.
Development of a novel hybrid method combining online NIR PLS with a potency soft sensor estimation.

Main Results:

The hybrid method provides robust online potency prediction.
It effectively minimizes deviations caused by uncaptured sources of variability.
The approach reduces the need for frequent model updates and maintenance.

Conclusions:

The hybrid method enhances the reliability of online potency estimation in continuous manufacturing.
Integrating process knowledge with NIR data improves prediction accuracy and robustness.
This approach supports efficient process development and cross-site method transfer.