Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data is used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10 L bioreactor model of Lonza's large-scale (up to 20,000 L) fed-batch cell culture processes.Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10 L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10 L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements.Keywords: Cell line development, Chinese hamster ovary cells, whole cell MALDI-ToF mass spectrometry, PLS-DA modelling, cell line prediction 3 IntroductionThe majority of recombinant protein biopharmaceuticals are produced from cultured mammalian cells (Walsh, 2010), with the most commonly used industrial mammalian cell host being the Chinese hamster ovary (CHO) cell (Kim et al., 2012). Despite the development of high throughput methods that allow the screening of many recombinant cell lines to isolate those with desirable phenotypes (e.g. high growth and productivity), the ability of such methods to select or predict the performance of a given cell line at manufacturing scale remains limited with the best cell lines at a manufacturing scale often distributed across the phenotypic performance of cell lines at smaller-scale (Porter et al., 2010a;Porter et al., 2010b). As a result, early use of a simple productivity based approach does not necessarily allow the identification of high producers in the population of cell lines, and some potentially high producers are discarded early in the process (Porter et al., 2010b). In order to address this issue, a number of proteomic, transcriptomic and metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (e.g. Clarke et al., 2011;Clarke...

in Wiley InterScience (www.interscience.wiley.com).An awareness of the likely future behavior of a batch or a fed-batch fermentation process is valuable information that can be exploited to improve product consistency and maximize profitability. For example, by making operational policy changes in a feedforward control sense, improved consistency can be facilitated, while prior knowledge of batch productivity, or the end time, can help determine the downstream processing configuration and upstream process scheduling. In this article, forecasting methods based on multivariate batch statistical data analysis procedures are contrasted with case-based reasoning (CBR). Additionally, the importance of appropriate statistical data prescreening and the choice of a suitable metric for case selection are investigated. Two industrial case studies are considered, a fedbatch pharmaceutical fermentation and a batch beer fermentation process. It is demonstrated that, following appropriate statistical prescreening of the data, in terms of forecasting performance, CBR is comparable to linear projection to latent structures (PLS), for the more straightforward problem, i.e., the batch beer fermentation, while for the more complex case-the pharmaceutical process-CBR exhibits enhanced performance over PLS.

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF MS) has been exploited extensively in the field of microbiology for the characterisation of bacterial species, the detection of biomarkers for early disease diagnosis and bacterial identification. Here, the multivariate data analysis technique of partial least squares-discriminant analysis (PLS-DA) was applied to 'intact cell' MALDI-ToF MS data obtained from Escherichia coli cell samples to determine if such an approach could be used to distinguish between, and characterise, different growth phases. PLS-DA is a technique that has the potential to extract systematic variation from large and noisy data sets by identifying a lower-dimensional subspace that contains latent information. The application of PLS-DA to the MALDI-ToF data obtained from cells at different stages of growth resulted in the successful classification of the samples according to the growth phase of the bacteria cultures. A further outcome of the analysis was that it was possible to identify the mass-to-charge (m/z) ratio peaks or ion signals that contributed to the classification of the samples. The Swiss-Prot/TrEMBL database and primary literature were then used to provisionally assign a small number of these m/z ion signals to proteins, and these tentative assignments revealed that the major contributors from the exponential phase were ribosomal proteins. Additional assignments were possible for the stationary phase and the decline phase cultures where the proteins identified were consistent with previously observed biological interpretation. In summary, the results show that MALDI-ToF MS, PLS-DA and a protein database search can be used in combination to discriminate between 'intact cell' E. coli cell samples in different growth phases and thus could potentially be used as a tool in process development in the bioprocessing industry to enhance cell growth and cell engineering strategies.

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