Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow

Aydin Golabgir; Jahir M Gutierrez; Hooman Hefzi; Shangzhong Li; Bernhard O Palsson; Christoph Herwig; Nathan E Lewis

doi:10.1016/j.biotechadv.2016.02.011

Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow

Biotechnol Adv. 2016 Sep-Oct;34(5):621-633. doi: 10.1016/j.biotechadv.2016.02.011. Epub 2016 Mar 3.

Authors

Aydin Golabgir¹, Jahir M Gutierrez², Hooman Hefzi², Shangzhong Li², Bernhard O Palsson³, Christoph Herwig⁴, Nathan E Lewis⁵

Affiliations

¹ Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorferstrasse 1a-166/4, A-1060, Vienna, Austria.
² Department of Bioengineering, University of California, San Diego, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego School of Medicine, San Diego, CA, USA.
³ Department of Bioengineering, University of California, San Diego, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego School of Medicine, San Diego, CA, USA; Department of Pediatrics, University of California, San Diego, CA, USA; The Jacobs School of Engineering CHO Systems Biology Center, University of California, San Diego, CA, USA.
⁴ Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorferstrasse 1a-166/4, A-1060, Vienna, Austria; CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna University of Technology, Gumpendorferstrasse 1a-166/4, A-1060, Vienna, Austria. Electronic address: christoph.herwig@tuwien.ac.at.
⁵ Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego School of Medicine, San Diego, CA, USA; Department of Pediatrics, University of California, San Diego, CA, USA; The Jacobs School of Engineering CHO Systems Biology Center, University of California, San Diego, CA, USA. Electronic address: nlewisres@ucsd.edu.

PMID: 26948029
DOI: 10.1016/j.biotechadv.2016.02.011

Abstract

The scientific literature concerning Chinese hamster ovary (CHO) cells grows annually due to the importance of CHO cells in industrial bioprocessing of therapeutics. In an effort to start to catalogue the breadth of CHO phenotypes, or phenome, we present the CHO bibliome. This bibliographic compilation covers all published CHO cell studies from 1995 to 2015, and each study is classified by the types of phenotypic and bioprocess data contained therein. Using data from selected studies, we also present a quantitative meta-analysis of bioprocess characteristics across diverse culture conditions, yielding novel insights and addressing the validity of long held assumptions. Specifically, we show that bioprocess titers can be predicted using indicator variables derived from viable cell density, viability, and culture duration. We further identified a positive correlation between the cumulative viable cell density (VCD) and final titer, irrespective of cell line, media, and other bioprocess parameters. In addition, growth rate was negatively correlated with performance attributes, such as VCD and titer. In summary, despite assumptions that technical diversity among studies and opaque publication practices can limit research re-use in this field, we show that the statistical analysis of diverse legacy bioprocess data can provide insight into bioprocessing capabilities of CHO cell lines used in industry. The CHO bibliome can be accessed at http://lewislab.ucsd.edu/cho-bibliome/.

Keywords: Bibliome; Bioprocess; Chinese hamster ovary; Meta-analysis; Phenome; Statistical analysis.

Publication types

Meta-Analysis
Review
Research Support, Non-U.S. Gov't

MeSH terms

Animals
Biomedical Research / statistics & numerical data*
Bioreactors*
CHO Cells* / cytology
CHO Cells* / metabolism
CHO Cells* / physiology
Cell Count
Cell Survival
Cricetinae
Cricetulus
Data Mining
Databases, Factual*
Phenotype