ABSTRACT
Site-specific integration (SSI) technology has emerged as an effective approach by the pharmaceutical industry for the development of recombinant Chinese hamster ovary (CHO) cell lines. While SSI systems have been demonstrated to be effective for the development of CHO cell lines, they can be limiting in terms of both transgene expression and in the case of multi-specifics, the ability to generate the correct product of interest. To maximize the performance of Pfizer's dual SSI expression system for expressing monoclonal and multi-specific antibodies, we used a novel approach to investigate the positional effect of transgenes within expression vectors by engineering nucleotide polymorphisms (NP)s to use as biomarkers to track the level of transcript output from each expression vector position. We observed differences in transcript level for two different transgenes across all four expression vector positions interrogated. We then applied these learnings to rationally design expression vectors for six different mAbs and a multi-specific antibody. We showed enhanced productivity and optimal product quality when compared to a conventional expression vector topology. The learnings gained here can potentially aid in the determination of optimal vector topologies for several IgG-like multi-specific formats.
