Robust factor selection in early cell culture process development for the production of a biosimilar monoclonal antibody

被引：27

作者：

Sokolov, Michael ^{[1
]}

Ritscher, Jonathan ^{[1
]}

MacKinnon, Nicola ^{[2
]}

Bielser, Jean-Marc ^{[2
]}

Bruhlmann, David ^{[2
]}

Rothenhausler, Dominik ^{[3
]}

Thanei, Gian ^{[3
]}

Soos, Miroslav ^{[4
]}

Stettler, Matthieu ^{[2
]}

Souquet, Jonathan ^{[2
]}

Broly, Herve ^{[2
]}

Morbidelli, Massimo ^{[1
]}

Butte, Alessandro ^{[1
]}

机构：

[1] ETH, Inst Chem & Bioengn, Zurich, Switzerland

[2] Merck Serono SA, Biotech Proc Sci, Corsier Sur Vevey, Switzerland

[3] ETH, Dept Math, Seminar Stat, Zurich, Switzerland

[4] UCT Prague, Bioengn & Adv Funct Mat Lab, Prague, Czech Republic

来源：

BIOTECHNOLOGY PROGRESS | 2017年 / 33卷 / 01期

关键词：

multivariate data analysis; biosimilars; process screening; high-throughput process development; principal component analysis; decision trees; PRINCIPAL COMPONENT ANALYSIS; MULTIVARIATE DATA-ANALYSIS; N-GLYCOSYLATION; BATCH; PREDICTION; IDENTIFICATION; TECHNOLOGY; PARAMETERS; QUALITY;

D O I：

10.1002/btpr.2374

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

This work presents a multivariate methodology combining principal component analysis, the Mahalanobis distance and decision trees for the selection of process factors and their levels in early process development of generic molecules. It is applied to a high throughput study testing more than 200 conditions for the production of a biosimilar monoclonal antibody at microliter scale. The methodology provides the most important selection criteria for the process design in order to improve product quality towards the quality attributes of the originator molecule. Robustness of the selections is ensured by cross-validation of each analysis step. The concluded selections are then successfully validated with an external data set. Finally, the results are compared to those obtained with a widely used software revealing similarities and clear advantages of the presented methodology. (c) 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:181-191, 2017

引用

页码：181 / 191

页数：11

共 38 条

[1] Principal component analysis [J].