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

AbstractRaman 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. T he model showed very good predictive capabilities, withR2 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.

Source: Biotechnology Progress - March 27, 2024 Category: Biotechnology Authors: Julie Østerby Madsen, Sebastian Olivier Nymann Topalian, Mikkel Fog Jacobsen, Tommy Skovby, Krist V. Gernaey, Allan S. Myerson, John Woodley Tags: RESEARCH ARTICLE Source Type: research