Microarray-based gene expression analysis as a process characterization tool to establish comparability of complex...

Whole-cell immunotherapies and other cellular therapies have shown promising results in clinical trials. Due to the complex nature of the whole cell product and of the sometimes limited correlation of clinical potency with the proposed mechanism of action, these cellular immunotherapy products are generally not considered well characterized. Therefore, one major challenge in the product development of whole cell therapies is the ability to demonstrate comparability of product after changes in the manufacturing process. Such changes are nearly inevitable with increase in manufacturing experience leading to improved and robust processes that may have higher commercial feasibility. In order to comprehensively assess the impact of the process changes on the final product, and thus establish comparability, a matrix of characterization assays (in addition to lot release assays) assessing the various aspects of the cellular product are required. In this study, we assessed the capability of DNA-microarray-based, gene-expression analysis as a characterization tool using GVAX cancer immunotherapy cells manufactured by Cell Genesys, Inc. The GVAX immunotherapy product consists two prostate cancer cell lines (CG1940 and CG8711) engineered to secrete human GM-CSF. To demonstrate the capability of the assay, we assessed the transcriptional changes in the product when produced in the presence or absence of fetal bovine serum, and under normal and hypoxic conditions, both changes intended to stress the cell lines. We then assessed the impact of an approximately 10-fold process scale-up on the final product at the transcriptional level. These data were used to develop comparisons and statistical analyses suitable for characterizing culture reproducibility and cellular product similarity. Use of gene-expression data for process characterization proved to be a reproducible and sensitive method for detecting differences due to small or large changes in culture conditions as might be encountered in process scale-up or unanticipated bioprocess failures. Gene expression analysis demonstrated that cell products of representative lots under the same production process and at the same production scale were statistically identical. Large process changes that resulted from the artificial stress conditions used (absence of FBS and induction of hypoxia) displayed profoundly different gene expression patterns. We propose the use of simple t-test analysis in combination with the herein introduced expression ratio with mean intensity (ERMI) analysis as useful tools for process characterization by global gene expression analysis.