title: A hybrid approach identifies metabolic signatures of high-producers for chinese hamster ovary clone selection and process optimization.
abstract: In-depth characterization of high-producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data-driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO-K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high-producing CHO-K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high-producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched-chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high-content selection of CHO producer clones in a high-throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high-performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005-2019. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Pharma Research and Early Development, Cell Culture Research, Roche Innovation Center Penzberg, Roche Diagnostics GmbH, Nonnenwald 2, D-82377 Penzberg, Germany. oliver.popp@roche.com.
authors.forename: Oliver
authors.initials: O
authors.lastname: Popp
authors.affiliation: Insilico Biotechnology AG, Stuttgart, Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Müller
authors.affiliation: Pharma Research and Early Development, Cell Culture Research, Roche Innovation Center Penzberg, Roche Diagnostics GmbH, Nonnenwald 2, D-82377 Penzberg, Germany.
authors.forename: Katharina
authors.initials: K
authors.lastname: Didzus
authors.affiliation: Pharma Research and Early Development, Cell Culture Research, Roche Innovation Center Penzberg, Roche Diagnostics GmbH, Nonnenwald 2, D-82377 Penzberg, Germany.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Paul
authors.affiliation: Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Penzberg, Roche Diagnostics GmbH, Penzberg, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Lipsmeier
authors.affiliation: Insilico Biotechnology AG, Stuttgart, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Kirchner
authors.affiliation: Insilico Biotechnology AG, Stuttgart, Germany.
authors.forename: Jens
authors.initials: J
authors.lastname: Niklas
authors.affiliation: Insilico Biotechnology AG, Stuttgart, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Mauch
authors.affiliation: Pharma Research and Early Development, Cell Culture Research, Roche Innovation Center Penzberg, Roche Diagnostics GmbH, Nonnenwald 2, D-82377 Penzberg, Germany.
authors.forename: Nicola
authors.initials: N
authors.lastname: Beaucamp
mesh_terms: D000818:Animals; D016466:CHO Cells / Q000166:cytology* / Q000378:metabolism*; D002999:Clone Cells / Q000166:cytology* / Q000378:metabolism*; D006224:Cricetinae; D003412:Cricetulus; D020869:Gene Expression Profiling; D023281:Genomics; D060847:Metabolic Engineering / Q000379:methods*; D053858:Metabolic Networks and Pathways; D011994:Recombinant Proteins / Q000378:metabolism*
chemical_list: D011994:Recombinant Proteins
keywords: CHO Cells; clone Selection; metabolic flux analysis; process Optimization