Metaheuristic approaches in biopharmaceutical process development data analysis.
Bioprocess Biosyst Eng
; 42(9): 1399-1408, 2019 Sep.
Article
en En
| MEDLINE
| ID: mdl-31119388
ABSTRACT
There is a growing interest in mining and handling of big data, which has been rapidly accumulating in the repositories of bioprocess industries. Biopharmaceutical industries are no exception; the implementation of advanced process control strategies based on multivariate monitoring techniques in biopharmaceutical production gave rise to the generation of large amounts of data. Real-time measurements of critical quality and performance attributes collected during production can be highly useful to understand and model biopharmaceutical processes. Data mining can facilitate the extraction of meaningful relationships pertaining to these bioprocesses, and predict the performance of future cultures. This review evaluates the suitability of various metaheuristic methods available for data pre-processing, which would involve the handling of missing data, the visualisation of the data, and dimension reduction; and for data processing, which would focus on modelling of the data and the optimisation of these models in the context of biopharmaceutical process development. The advantages and the associated challenges of employing different methodologies in pre-processing and processing of the data are discussed. In light of these evaluations, a summary guideline is proposed for handling and analysis of the data generated in biopharmaceutical process development.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Productos Biológicos
/
Industria Farmacéutica
/
Heurística
/
Modelos Teóricos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Bioprocess Biosyst Eng
Asunto de la revista:
BIOTECNOLOGIA
/
ENGENHARIA BIOMEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Reino Unido