Abstract: Methods for facilitating selection of cell lines for production of recombinant proteins are disclosed. In particular, disclosed is the use of machine learning models trained on multiomics data to predict one or more values indicative of the titre and/or quality of a recombinant protein expressed by different cell lines, enabling ranking the cell lines based on the predicted values and selecting those predicted to produce the recombinant protein with higher titre and/or higher quality.

Abstract: Herein is reported a novel, high throughput suitable method for the characterization and identification as well as selection of recombinant cell clones expressing a bispecific antibody with high titers and low levels of antibody-related side products. The method according to the current invention uses the binding signals of an immunoassay-based screening of recombinant cell clone cultivation supernatants for isolated as well as simultaneous binding to the respective targets of the antibody. This data allows for the selection of high producer cell clones with desired product profile.

Abstract: High throughput cultivation systems are used in pharmaceutical research and development. In this connection, samples are taken and analyzed for important parameters using external analysis. The results of the analysis serve to assess the cultivation process and provide important information about the process. Especially with cultivations carried out in parallel, the manual effort of sample preparation is great and can lead to errors. In order to avoid the need for sampling and thus to minimize the errors, a method is described in the present patent application which makes desired target parameters accessible in the form of soft sensors by means of previously recorded process variables. Herein is described a method for determining process-relevant parameters in CHO processes (Chinese hamster ovary) in high-throughput cultivations, in particular glucose, lactate and the live cell density or the live cell volume.

Abstract: Herein is reported a method for determining if process data acquired during the cultivation of a mammalian or bacterial cell is affected by a problem comprising the steps of (i) fitting the process data acquired during the cultivation of a mammalian or bacterial cell clone expressing a recombinant, heterologous polypeptide in a metabolic model generated for the same mammalian or bacterial cell expressing the same recombinant, heterologous polypeptide, and (ii) determining that the cultivation is affected by a problem if the modeled fit shows an offset with respect to the raw data of more than 10%, or the modeled fit has a chi2 value determined by a Pearson's chi-squared test of more than 5.