Use of neural networks in the mathematical modelling of the enzymic synthesis of amoxicillin catalysed by penicillin G acylase immobilized in chitosan

This study focuses in the mathematical modelling of the enzymic synthesis of amoxicillin by the reaction of p-hydroxyphenylglycine methyl ester and 6-aminopenicillanic acid (6APA), catalyzed by penicillin G acylase (PGA) immobilized on glutaraldehyde-chitosan, at 25°C and pH 6.5. Previous work on th...

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Bibliographic Details
Published in:World journal of microbiology & biotechnology 2008-09, Vol.24 (9), p.1761-1767
Main Authors: Silva, James A, Costa Neto, Edilson Holanda, Adriano, Wellington S, Ferreira, Andrea L. O, Gonçalves, Luciana R. B
Format: Article
Language:English
Subjects:
Antibiotics
Applied Microbiology
Batch reactors
Biocatalysts
Biochemistry
Bioconversions. Hemisynthesis
Biological and medical sciences
Biomedical and Life Sciences
Biotechnology
Chemical synthesis
Control algorithms
Environmental Engineering/Biotechnology
Enzymes
Fundamental and applied biological sciences. Psychology
Immobilization of enzymes and other molecules
Immobilization techniques
Life Sciences
Mathematical models
Methods. Procedures. Technologies
Microbiology
Neural networks
Original Paper
Penicillin
Studies
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Summary:This study focuses in the mathematical modelling of the enzymic synthesis of amoxicillin by the reaction of p-hydroxyphenylglycine methyl ester and 6-aminopenicillanic acid (6APA), catalyzed by penicillin G acylase (PGA) immobilized on glutaraldehyde-chitosan, at 25°C and pH 6.5. Previous work on the kinetics and mechanism of reaction showed that the use of neural networks seems to be an interesting alternative to simulate experimental data of antibiotic production. Therefore, two feedforward neural networks, with one hidden layer, were trained and used to forecast the rates of amoxicillin and p-hydroxyphenylglycine (POHPG) net production. First of all, some parameters that affect the network performed were investigated, such as the number of nodes between the input and hidden layers and the number of interactions during the learning phase. Afterwards, hybrid models that coupled artificial neural networks to mass-balance equations were used to reproduce the performance of batch reactors for the production of amoxicillin. This approach provided accurate results, within the range of substrate concentration studied.
ISSN:0959-3993
1573-0972
DOI:10.1007/s11274-008-9670-1