Statistical analysis of data from limiting dilution cloning to assess monoclonality in generating manufacturing cell lines. (17th May 2016)

• Record Type: Journal Article
• Authors: Quiroz, Jorge; Tsao, Yung‐Shyeng
• Is Part Of: Biotechnology progress. Volume 32:Number 4(2016:Jul./Aug.)
• Page Start: 1061
• Page End: 1068
• Publication Date: 2016-05-17
• Subjects: limiting dilution cloning -- manufacturing cell lines -- monoclonality in one round of subcloning -- monoclonality in two consecutive subclonings -- Poisson distribution -- recombinant protein production; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.2290
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract : Assurance of monoclonality of recombinant cell lines is a critical issue to gain regulatory approval in biological license application (BLA). Some of the requirements of regulatory agencies are the use of proper documentations and appropriate statistical analysis to demonstrate monoclonality. In some cases, one round may be sufficient to demonstrate monoclonality. In this article, we propose the use of confidence intervals for assessing monoclonality for limiting dilution cloning in the generation of recombinant manufacturing cell lines based on a single round. The use of confidence intervals instead of point estimates allow practitioners to account for the uncertainty present in the data when assessing whether an estimated level of monoclonality is consistent with regulatory requirements. In other cases, one round may not be sufficient and two consecutive rounds are required to assess monoclonality. When two consecutive subclonings are required, we improved the present methodology by reducing the infinite series proposed by Coller and Coller (Hybridoma 1983;2:91–96) to a simpler series. The proposed simpler series provides more accurate and reliable results. It also reduces the level of computation and can be easily implemented in any spreadsheet program like Microsoft Excel. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1061–1068, 2016