open access publication

Article, 2024

Raman spectroscopy and one‐dimensional convolutional neural network modeling as a real‐time monitoring tool for in vitro transaminase‐catalyzed synthesis of a pharmaceutically relevant amine precursor

Biotechnology Progress, ISSN 8756-7938, 1520-6033, Volume 40, 3, Page e3444, 10.1002/btpr.3444

Contributors

Madsen, Julie Østerby 0000-0002-3463-0583 [1] [2] Topalian, Sebastian Olivier Nymann 0000-0002-2339-2073 [1] Jacobsen, Mikkel Fog [3] Skovby, Tommy [4] Gernaey, Krist Victor Bernard 0000-0002-0364-1773 [1] Myerson, Allan S [2] Woodley, John Michael 0000-0002-7976-2483 (Corresponding author) [1]

Affiliations

  1. [1] Technical University of Denmark
    2. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD];
  2. [2] Massachusetts Institute of Technology
    3. [NORA names: United States; America, North; OECD];
  3. [3] Lundbeck (Denmark)
    4. [NORA names: Lundbeck; Private Research; Denmark; Europe, EU; Nordic; OECD];
  4. [4] Chemical Production Development, H. Lundbeck A/S, Nykøbing Sjælland, Denmark
    5. [NORA names: Denmark; Europe, EU; Nordic; OECD]

Abstract

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions.

Keywords

Raman spectra, Raman spectroscopy, acetylcholine receptors, allosteric modulator of nicotinic acetylcholine receptors, amine intermediate, amine precursors, amines, biocatalytic reactions, capability, concentration, conversion, convolutional neural network, data, dataset, independent datasets, intermediate, ketones, model, modulator of nicotinic acetylcholine receptors, monitoring tool, network, neural network, nicotinic acetylcholine receptors, one-dimensional convolutional neural network, pharmaceuticals, positive allosteric modulators, positive allosteric modulators of nicotinic acetylcholine receptors, potential, potential of Raman spectroscopy, precursor, predictive capability, raw data, reaction, real-time monitoring tool, receptors, spectra, spectroscopy, substrate concentration, synthesis, tools, values

Funders

  • Massachusetts Institute of Technology
  • Lundbeck (Denmark)
  • Technical University of Denmark

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