Implementation of a high throughput automated platform for residual DNA quantitation.

Host cell DNA is an impurity from cell-based manufacturing processes that must be controlled and monitored to ensure drug purity and safety. Conventional methods for measurement of residual host cell DNA in therapeutic protein require numerous preparations of plates, DNA extraction from the protein...

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Bibliographic Details
Main Authors: Shruti B Patel, Jon Jurica, Xiaoqing Hua
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0322133
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Summary:Host cell DNA is an impurity from cell-based manufacturing processes that must be controlled and monitored to ensure drug purity and safety. Conventional methods for measurement of residual host cell DNA in therapeutic protein require numerous preparations of plates, DNA extraction from the protein samples, followed by quantification of the extracted DNA using real-time PCR (qPCR). Preparation of plates for extraction is the most laborious step, including numerous manual steps such as sample dilution, standard curve preparation, as well as reagent and sample plate preparation. Additionally, much of the work needs to be performed in a biosafety cabinet to avoid contamination. In this study, a robotic platform using a Gilson liquid handler for plate preparation for rDNA extraction is presented. This automated workflow is not only high throughput, but also shows reproducibility that is equivalent or better as compared to manual workflows. Moreover, this approach is faster than traditional extraction and reduces the risk of human error and variability and eliminates the need for manual pipetting and plate preparation. In this study, automated and manual workflows were performed side-by-side in triplicate from different purification steps from bioreactor to ultrafiltration step. Day to day variability, matrix interference, and spike recovery were assessed to demonstrate the robustness of the automated workflow.
ISSN:1932-6203