Identification of infectious viruses for risk‐based virus testing of CHO unprocessed bulk using next‐generation sequencing

Author:

Hsu Tiffany1ORCID,Talley Mary Jo1,Yang Ping1,Geiselhoeringer Angela1,Yang Cindy1,Gorla Aditya1,Rahman M. Julhasur1,Silva Lindsey1,Chen Dayue1,Yang Bin1ORCID

Affiliation:

1. Purification, Microbiology, and Virology Genentech, a Member of the Roche Group South San Francisco California USA

Abstract

AbstractIt is important to increase manufacturing speed to make medicines more widely available. One bottleneck for CHO‐based drug substance release is the in vitro viral (IVV) cell‐based assay on unprocessed bulk. To increase process speed, we evaluate the suitability of replacing the IVV cell‐based assay with next‐generation sequencing (NGS). First, we outline how NGS is currently used in the pharmaceutical industry, and how it may apply to CHO virus testing. Second, we examine CHO virus contamination history. Since prior virus contaminants can replicate in the production bioreactor, we perform a literature search and classify 159 viruses as high, medium, low, or unknown risk based on their ability to infect CHO cells. Overall, the risk of virus contamination during the CHO manufacturing process is low. Only six viruses were reported to have contaminated CHO bioprocesses over the past several decades, and were primarily caused by fetal bovine serum or cell culture components. These virus contamination events can be mitigated through limitation and control of raw materials, combined with virus testing and virus clearance technologies. The list of CHO infectious viruses provides a starting framework for virus safety risk assessment and NGS development. Furthermore, ICH Q5A (R2) includes NGS as a molecular method for adventitious agent testing, paving a path forward for modernizing CHO virus testing.

Publisher

Wiley

Reference185 articles.

1. ICH Harmonised Tripartite Guideline Pharmaceutical Development Q8 (R2).2009.https://database.ich.org/sites/default/files/Q8%28R2%29%20Guideline.pdf

2. A Systematic Approach for the Evaluation, Validation, and Implementation of Automated Colony Counting Systems

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