open access publication

Article, 2024

Modernizing non-classical protein crystallization through industry 4.0: Advanced monitoring and modelling utilizing process analytical technology

Chemical Engineering Research and Design, ISSN 1744-3563, 0263-8762, Volume 204, Pages 382-389, 10.1016/j.cherd.2024.02.037

Contributors

Jul-Jørgensen, Isabella 0000-0003-1335-3772 (Corresponding author) [1] [2] Oliver, R. [1] Gernaey, Krist Victor Bernard 0000-0002-0364-1773 [2] Hundahl, Christian Ansgar [1]

Affiliations

  1. [1] Novo Nordisk (Denmark)
    2. [NORA names: Novo Nordisk; Private Research; Denmark; Europe, EU; Nordic; OECD];
  2. [2] Technical University of Denmark
    3. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

Modernizing manufacturing processes of pharmaceutical drug products with advanced monitoring and modelling can aid in the transition towards Industry 4.0 with the benefit of increased productivity. This study investigated the use of process analytical technology in combination with partial least squares (PLS) regressions to create two soft sensors to predict the mass fraction of crystallised active pharmaceutical ingredient (API) and mass fraction of dissolved API during a non-classical protein crystallization with amorphous precursors. The PLS model for predicting the amount of crystalline API was based on Raman spectra, chord length distributions and turbidity data using small-angle X-ray scattering as a reference method. The model had a root mean square error on cross-validation (RMSECV) of 5 %. The model predicting mass fraction of dissolved API was based only on the Raman spectra and used high performance liquid chromatography as reference method. This model had a RMSECV of 3 % A two-step nucleation model was fitted to the predictions from the sensors and showed good agreement between data and model with a root mean square error of 2 %.

Keywords

RMSECV, Raman spectra, X-ray scattering, active pharmaceutical ingredients, advanced monitoring, amorphous precursor, amount, analytical technology, benefits, benefits of increased productivity, chord, chord length distribution, chromatography, combination, cross-validation, crystal, crystalline active pharmaceutical ingredients, data, distribution, drug products, error, high-performance liquid chromatography, industry, ingredients, least squares, length distribution, liquid chromatography, mass fraction, method, model, modern manufacturing processes, monitoring, nucleation, nucleation model, partial least squares, partial least squares model, performance liquid chromatography, pharmaceutical drug products, pharmaceutical ingredients, precursor, prediction, process, process analytical technology, production, protein crystals, reference, reference method, regression, root, root mean square error, scattering, sensor, small-angle X-ray scattering, soft sensor, spectra, square, square error, study, technology, transition, turbidity, turbidity data

Funders

  • Innovation Fund Denmark
  • Novo Nordisk (Denmark)

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