Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Full text
Author(s):
Takahashi, Maria Beatriz [1] ; Leme, Jaci [2] ; Caricati, Celso Pereira [2] ; Tonso, Aldo [3] ; Fernandez Nunez, Eutimio Gustavo [1, 3] ; Rocha, Jose Celso [1]
Total Authors: 6
Affiliation:
[1] Univ Estadual Paulista, Dept Ciencias Biol, BR-19806900 Assis, SP - Brazil
[2] Inst Butantan, Lab Especial Pesquisa & Desenvolvimento Imunol Ve, BR-05503900 Sao Paulo, SP - Brazil
[3] Univ Sao Paulo, Escola Politecn, Dept Engn Quim, Lab Celulas Animais, BR-05503900 Sao Paulo, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Bioprocess and Biosystems Engineering; v. 38, n. 6, p. 1045-1054, JUN 2015.
Web of Science Citations: 9
Abstract

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV-Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV-Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 +/- A 0.14 mM), glutamate (0.02 +/- A 0.02 mM), glucose (1.11 +/- A 1.70 mM), lactate (0.84 +/- A 0.68 mM) and viable cell concentrations (1.89 10(5) +/- A 1.90 10(5) cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV-VIS sensor development, and decreases cost related to nutrients and metabolite quantifications. (AU)