Technical Journal, Vol. 18 No. 3, 2024.
Original scientific paper
https://doi.org/10.31803/tg-20230626220812
Artificial Neural Network Models for Solution Concentration Measurement during Cooling Crystallization of Ceritinib
Ivan Vrban
; Teva Pharmaceutical Industries Ltd. Prilaz Baruna Filipovića 25, 10000 Zagreb, Croatia
*
Damir Šahnić
; Teva Pharmaceutical Industries Ltd. Prilaz Baruna Filipovića 25. 10000 Zagreb, Croatia
Nenad Bolf
orcid.org/0000-0001-7005-7561
; University of Zagreb, Faculty of Chemical Engineering and Technology, 10000 Zagreb, Croatia
* Corresponding author.
Abstract
The development of a quantitative in-line UV spectroscopic method for monitoring of solute concentration during the crystallization process of the active pharmaceutical ingredient (API), ceritinib is described. The method is based on artificial neural networks (ANN). A seeded cooling crystallization process of ceritinib from tetrahydrofuran was studied as a model system. The model was constructed from collected ATR-UV spectra and temperature records within the metastable zone. The collected spectra were preprocessed with the first derivative using the Savitzky-Golay filter. ANN models with different architectures were created and the optimal architecture was chosen based on the root mean square error of prediction (RMSEP) criterion. In addition, ANN models were compared with the models obtained by the linear partial least squares regression (PLSR). Due to the nonlinear relationship in the data set, ANN models predict the solution concentration with higher accuracy compared to linear models. The developed models were successfully used in real-time solution concentration measurement during ceritinib crystallization along with a supersaturation control module developed in-house.
Keywords
artificial neural network; ATR-UV/Vis; ceritinib; concentration real-time measurement; cooling crystallization; supersaturation control
Hrčak ID:
319173
URI
Publication date:
5.9.2024.
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