Defense Date

12-19-2023

Graduation Date

Spring 5-2024

Availability

One-year Embargo

Submission Type

dissertation

Degree Name

PhD

Department

Pharmaceutics

School

School of Pharmacy

Committee Chair

Carl A. Anderson, PhD

Committee Member

James K. Drennen, III, PhD

Committee Member

Kevin Tidgewell, PhD

Committee Member

Rehana K. Leak, PhD

Committee Member

Alexandre Ambrogelly, PhD

Keywords

chemometrics, process analytical technology (PAT), iterative optimization technology (IOT), net analyte signal (NAS), model diagnostics, wavelength selection, calibration-free

Abstract

The expansion of spectroscopic process analytical technology (PAT) tools within the pharmaceutical industry has the potential to elevate the current state-of-the-art of pharmaceutical manufacturing by offering opportunities for reduced quality testing times, enhanced process control, and greater production flexibility. Spectroscopic PAT tools are dependent on multivariate models to extract the relevant information from the spectral outputs. However, there is a substantial calibration burden for developing and maintaining these multivariate models that discourages the application of PAT, despite the encouragement from regulators. This has led to an interest in calibration-free methods such as iterative optimization technology (IOT) for spectroscopic PAT that have a substantially reduced calibration burden. The application of IOT algorithms to spectroscopic PAT tools by the pharmaceutical industry is inhibited by two major limitations. First, generalized model diagnostics are lacking for IOT algorithms. Second, IOT algorithms have limited prediction robustness when challenged by chemical and process variations. Current strategies available within the scientific literature to improve prediction accuracy and robustness of IOT algorithms unfortunately require appropriate . This moves away from the calibration-free intentions of the base IOT algorithm.

To address these limitations and advance IOT algorithms, this dissertation proposes the use of the net analyte signal (NAS) vector as a basis for a model diagnostic and calibration-free wavelength selection method that are both applicable to IOT algorithms. This dissertation successfully advances the state of IOT algorithms with the introduction of the NAS theta (NAS-T) model diagnostic and the calibration-free wavelength angle mapper (WAM) wavelength selection strategy.

Language

English

Additional Citations

  • A.J. Rish, S.R. Henson, M.A. Alam, Y. Liu, J.K. Drennen, C.A. Anderson, Development of calibration-free/minimal calibration wavelength selection for iterative optimization technology algorithms toward process analytical technology application, International Journal of Pharmaceutics, 614 (2022) 121463.
  • A.J. Rish, S.R. Henson, M.A. Alam, Y. Liu, J.K. Drennen, C.A. Anderson, Comparison between pure component modeling approaches for monitoring pharmaceutical powder blends with near-infrared spectroscopy in continuous manufacturing schemes, The AAPS Journal, 24 (2022) 82.
  • A.J. Rish, N.L. Velez-Silva, S. Henson, M.N. Hasan, J.K. Drennen, C.A. Anderson, Diagnostic development using net analyte signal for pure component modeling approaches, Chemometrics and Intelligent Laboratory Systems, (2023) 105007.
  • A.J. Rish, S.R. Henson, N.L. Velez-Silva, M.N. Hasan, J.K. Drennen, C.A. Anderson, Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures, International Journal of Pharmaceutics, 643 (2023) 123261.

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