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MAY 10, 2017 11:00 AM PDT

An ongoing evaluation of our transient expression systems; Can we predict the appropriate expression system for any drug modality?

Speaker
  • Scientist, Protein Technologies, Amgen, Inc.
    Biography
      Rich Altman has 29 years of experience working in the pharmaceutical industry. In 2016, he joined the Protein Technologies Mammalian Expression group at Amgen San Francisco, supporting biologics drug development. Prior to Amgen, he worked for several pharmaceutical companies on the cloning, expression, purification and characterization of recombinant proteins. This work supported both small-molecule high-throughput screening and protein therapeutic efforts. He received his MS degree from the University of Pittsburgh School of Medicine in the Department of Molecular Biology and Biochemistry.

    Abstract

    CHO cells are the predominant host for biotherapeutic protein expression with approximately 70% of licensed biologics manufactured in Chinese Hamster Ovary (CHO) cells.  The ability to express therapeutic candidates in CHO cells early in the drug development process is advantageous as it minimizes changes in protein quality and function when transitioning to production scale.  To address the lack of a robust and reproducible in-house transient CHO expression system, we incorporated the ExpiCHO™ transient expression system into our transient expression toolbox, joining the well-established HEK293-6E and Expi293™ systems.  With an ever-expanding array of protein formats, the next challenge is being able to strategically match a construct with an expression system.  We have utilized a diverse set of candidate proteins in an ongoing evaluation comparing material transiently produced in parallel in the HEK293-6E, ExpiCHO™ and Expi293™ systems for expression level and product quality.  We report these results and discuss their implications in how we prioritize our selection of expression systems.  Successful prioritization of an expression system for a drug modality will reduce parallel efforts and increase our throughput efficiency by reducing expression timelines


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