Use of Uniform Designs in Combination with Neural Networks for Viral Infection Process Development

Author(s)

Buenno, Lais Hara ^[1] ; Rocha, Jose Celso ^[1] ; Leme, Jaci ^[2] ; Caricati, Celso Pereira ^[2] ; Tonso, Aldo ^[3] ; Fernandez Nunez, Eutimio Gustavo ^{[1, 3]}

Affiliation

^[1] Univ Estadual Julio de Mesquita Filho, 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 Anim, BR-05508900 Sao Paulo, SP - Brazil

• Document type: Journal article
• Source: BIOTECHNOLOGY PROGRESS; v. 31, n. 2, p. 532-540, MAR-APR 2015.
• Web of Science Citations: 2

Abstract

This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34 degrees C, 37 degrees C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions. (c) 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:532-540, 2015 (AU)

• FAPESP's process: 14/03883-3 - Validation of uniform designs use in combination with neural networks in bioprocess
• Grantee: Laís Hara Buenno
• Support Opportunities: Scholarships in Brazil - Scientific Initiation