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Robust near-infrared modeling for pharmaceutical powder streams: External variable augmented iterative optimization

Natasha L Velez-Silva1, Adam J Rish1, James K Drennen1

  • 1Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States.

European Journal of Pharmaceutics and Biopharmaceutics : Official Journal of Arbeitsgemeinschaft Fur Pharmazeutische Verfahrenstechnik E.V
|January 17, 2025
PubMed
Summary
This summary is machine-generated.

A new method, external variable augmented iterative optimization technology (EVA-IOT), enhances drug prediction accuracy in continuous manufacturing. EVA-IOT improves robustness against process variations, offering a material-sparing alternative for quality monitoring.

Keywords:
Continuous manufacturing processIterative optimization algorithmMinimal calibrationModel robustnessProcess analytical technology

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Analytical Technology
  • Spectroscopy

Background:

  • Iterative optimization technology (IOT) algorithms are increasingly used in pharma for calibration-free process analytical technology.
  • Near-infrared (NIR) spectroscopy with IOT shows promise for monitoring continuous powder mixtures.
  • Dynamic process conditions and density variations in continuous manufacturing can reduce IOT method accuracy and robustness.

Purpose of the Study:

  • To introduce a novel method, EVA-IOT, to improve the prediction accuracy and robustness of IOT algorithms.
  • To address the limitations of IOT methods in dynamic continuous manufacturing environments.
  • To develop a material-sparing alternative to traditional calibration-intensive modeling approaches.

Main Methods:

  • Developed external variable augmented iterative optimization technology (EVA-IOT).
  • Incorporated non-chemical external variability shapes into pure component spectra.
  • Applied a density-augmented EVA-IOT method to quantify API in simulated continuous powder mixtures.
  • Utilized NIR spectra and decomposition methods to derive external variable structures.

Main Results:

  • EVA-IOT significantly enhanced active pharmaceutical ingredient (API) prediction accuracy and robustness.
  • The method demonstrated superior performance compared to standard IOT approaches under varying process conditions.
  • EVA-IOT achieved comparable prediction performance to global partial least square (PLS) models.
  • Achieved up to a 97% reduction in calibration burden compared to PLS models.

Conclusions:

  • EVA-IOT effectively improves the accuracy and robustness of IOT algorithms for continuous pharmaceutical manufacturing.
  • This method offers a material-sparing and calibration-efficient solution for real-time quality monitoring.
  • EVA-IOT represents a significant advancement for process analytical technology in pharmaceutical production.