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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
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Development of calibration-free/minimal calibration wavelength selection for iterative optimization technology

Adam J Rish1, Samuel R Henson1, Md Anik Alam2

  • 1Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA.

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|January 13, 2022
PubMed
Summary

Calibration-free Iterative Optimization Technology (IOT) algorithms effectively monitor active pharmaceutical ingredient (API) content and potency in continuous manufacturing (CM). A novel wavelength method improved IOT performance during non-steady state conditions.

Keywords:
Continuous manufacturingIterative optimization technologyPATPLSWavelength selection

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Area of Science:

  • Chemical Engineering
  • Analytical Chemistry
  • Pharmaceutical Manufacturing

Background:

  • Process Analytical Technology (PAT) using Near-Infrared (NIR) spectroscopy is crucial for monitoring continuous manufacturing (CM) processes.
  • Traditional multivariate models like Partial Least Squares (PLS) require extensive calibration, posing challenges for CM implementation.
  • Calibration-free or minimal calibration methods are increasingly sought to streamline PAT deployment in pharmaceutical CM.

Purpose of the Study:

  • To evaluate the efficacy of Iterative Optimization Technology (IOT) algorithms as a calibration-free PAT approach for CM.
  • To compare the performance of IOT against traditional PLS models for monitoring active pharmaceutical ingredient (API) content and potency.
  • To develop and implement a novel wavelength selection method to enhance IOT performance under dynamic CM conditions.

Main Methods:

  • Implemented IOT algorithms for quantitative (API content) and qualitative (potency trends) monitoring in a CM system.
  • Utilized pure component spectra as the sole requirement for IOT model calibration.
  • Developed and applied a variable importance in projection (VIP) scores-based wavelength selection method to address non-steady state challenges.

Main Results:

  • IOT algorithms successfully monitored API content and potency trends in the CM system.
  • The novel wavelength selection method significantly improved IOT prediction performance during non-steady state conditions.
  • IOT demonstrated comparable or superior performance to traditional PLS models with reduced calibration burden.

Conclusions:

  • IOT algorithms offer a viable and efficient calibration-free/minimal calibration alternative for PAT in pharmaceutical CM.
  • The developed wavelength selection strategy enhances the robustness of IOT for real-time process monitoring.
  • IOT algorithms present significant value and potential for widespread adoption in advanced pharmaceutical manufacturing.