Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method-Reference-Cited by-同舟云学术

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Published:2018-09-25 Issue:8 Volume:10 Page:1281-1290
ISSN:1942-0862
Container-title:mAbs
language:en
Short-container-title:mAbs

Author:

Sankar Kannan¹^ORCID,Hoi Kam Hon¹²^ORCID,Yin Yizhou¹³^ORCID,Ramachandran Prasanna⁴,Andersen Nisana⁴,Hilderbrand Amy⁴,McDonald Paul⁵,Spiess Christoph¹^ORCID,Zhang Qing¹²^ORCID

Affiliation:

1. Department of Antibody Engineering, Genentech, South San Francisco, CA, USA

2. Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA, USA

3. Institute for Bioscience and Biotechnology Research, Biological Sciences Graduate Program, University of Maryland, Rockville, MD, USA

4. Department of Analytical Development and Quality Control, Genentech, South San Francisco, CA, USA

5. Department of Purification Development and Bioprocess Development, Genentech, South San Francisco, CA, USA

Funder

Genentech, Inc

Publisher

Informa UK Limited

Subject

Immunology,Immunology and Allergy

Link

https://www.tandfonline.com/doi/pdf/10.1080/19420862.2018.1518887

Reference62 articles.

1. Therapeutic Proteins

2. Current progress in innovative engineered antibodies

3. Biopharmaceutical benchmarks 2014

4. Biophysical properties of the clinical-stage antibody landscape

5. Considerations for the development of therapeutic monoclonal antibodies

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