Using data analytics to accelerate biopharmaceutical process scale-up-Reference-Cited by-同舟云学术

Using data analytics to accelerate biopharmaceutical process scale-up

Published:2020-12 Issue: Volume:164 Page:107791
ISSN:1369-703X
Container-title:Biochemical Engineering Journal
language:en
Short-container-title:Biochemical Engineering Journal

Author:

Facco Pierantonio^ORCID,Zomer Simeone,Rowland-Jones Ruth C.,Marsh Douglas,Diaz-Fernandez Paloma,Finka Gary,Bezzo Fabrizio^ORCID,Barolo Massimiliano^ORCID

Funder

GlaxoSmithKline Research & Development

University of Padova

Publisher

Elsevier BV

Subject

Biomedical Engineering,Environmental Engineering,Bioengineering,Biotechnology

Reference30 articles.

1. Trends in clinical success rates;Smietana;Nat. Rev. Drug Discov.,2016

2. Comparative study of therapeutic antibody candidates derived from mini-pool and clonal cell lines;Fan;Biotechnol. Prog.,2017

3. The physical characterisation of a microscale parallel bioreactor platform with an industrial CHO cell line expressing an IgG4;Nienow;Biochem. Eng. J.,2013

4. Cell culture processes for monoclonal antibody production;Li;MAbs.,2010

5. Evaluation of piggyBac-mediated CHO pools to enable material generation to support GLP toxicology studies;Rajendra;Biotechnol. Prog.,2017

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