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

Julie Østerby Madsen, Sebastian Olivier Nymann Topalian, Mikkel Fog Jacobsen, Tommy Skovby, Krist V Gernaey, Allan S Myerson, John Woodley*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article numbere3444
JournalBiotechnology Progress
Volume40
Issue number3
Number of pages13
ISSN8756-7938
DOIs
Publication statusPublished - 2024

Keywords

  • Raman spectroscopy
  • Biocatalysis
  • Chemometrics
  • Real‐time monitoring
  • Transaminase

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