Premium
Sequential Multivariate Cell Culture Modeling at Multiple Scales Supports Systematic Shaping of a Monoclonal Antibody Toward a Quality Target
Author(s) -
Sokolov Michael,
Morbidelli Massimo,
Butté Alessandro,
Souquet Jonathan,
Broly Hervé
Publication year - 2018
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201700461
Subject(s) - multivariate statistics , computer science , process (computing) , quality (philosophy) , quality by design , scale (ratio) , critical quality attributes , set (abstract data type) , data mining , throughput , machine learning , multivariate analysis , industrial engineering , new product development , engineering , telecommunications , philosophy , physics , epistemology , marketing , quantum mechanics , business , wireless , programming language , operating system
The development of cell culture processes is highly complex and requires a large number of experiments on various scales to define the design space of the final process and fulfil the regulatory requirements. This work follows an almost complete process development cycle for a biosimilar monoclonal antibody, from high throughput screening and optimization to scale‐up and process validation. The key goal of this analysis is to apply tailored multivariate tools to support decision‐making at every stage of process development. A toolset mainly based on Principal Component Analysis, Decision Trees, and Partial Least Square Regression combined with a Genetic Algorithm is presented. It enables to visualize the sequential improvement of the high‐dimensional quality profile towards the target, provides a solid basis for the selection of effective process variables and allows to dynamically predict the complete set of product quality attributes. Additionally, this work shows the deep level of process knowledge which can be deduced from small scale experiments through such multivariate tools. The presented methodologies are generally applicable across various processes and substantially reduce the complexity, experimental effort as well as the costs and time of process development.