Seed trains for the production of biopharmaceuticals (antibodies & proteins for diagnostic and therapeutic purposes)

Corresponding cell lines and cultivation scales / -systems of any kind

A seed train generates an adequate number of cells for the inoculation of a production bioreactor. Fig. 1 shows an example:

The seed train steps have a significant impact on:

• product titer and cell growth in production scale,

• the success and the reproducibility of the seed train itself

Seed trains are time- and cost-intensive

Cell line changes in an existing facility require adaptions of the seed train protocol

Design of a new facility involves the choice of the optimal seed train scales in order to meet the future requirements of the cultivated cell lines

Programming of a tool which is able to mathematically describe different seed trains based on the user’s input of the corresponding seed train information

Analysis and optimization of existing seed trains

Design of new seed trains for new cell lines / new facilities

Tool structure:

Flexible program structure suitable for different seed trains

Modelling of cell growth, cell death, uptake of substrates and production of metabolites. User communication via GUI Layout Toolbox.

Suitable for different cell lines via entering corresponding model parame-ters (determined based on cultivation data using Nelder-Mead algorithm)

Optimization of modeled seed trains possible, e.g. regarding different cell passaging criteria

*Contact: bjoern.frahm@hs-owl.de

A software tool for biopharmaceutical seed train design and optimization

Tanja Hernández Rodríguez1, Simon Kern1,2, Ralf Pörtner2, Björn Frahm1*

1 Ostwestfalen-Lippe University of Applied Sciences, Biotechnology & Bioprocess Engineering, Lemgo, Germany2 Hamburg University of Technology, Bioprocess- and Biosystems Engineering, Hamburg, Germany

Fig. 2: Scheme of the seed train tool programming in MATLAB

Important medium con-

centrationsCell line

parameters for model

Starting conditions of

seed train

Seed traininformation

Modelling of seed train

Output, e.g. optimal points in time for cell

passaging

Optimization regarding different criteria

vial 5 mL 200 mL 10 L 500 L 3,500 L70 L 35 mL

production scale

1.5 L

Fig. 1: Seed train example for the inoculation of a 3,500 L bioreactor

Motivation

Importance of cell culture seed trains

Tool for seed train design, analysis and optimization

ResultsFields of application

Conclusions

The following Fig. 3 shows an example screen of the software tool:

The concentration courses are described by a first order system of ordinary differential equations and of Monod-type kinetics which describe the dependency of the rates on certain concentrations.

First tests of the tool included the application to two different cell lines, CHO-K1 (provided by Prof. Noll, Bielefeld University) and AGE1.HNAAT

(provided by proBioGen AG, Berlin), at lab scale in order to investigate the following topics:

Can the results / predictions of the tool be realized in seed train cultivations?

Is there a difference between the tool-designed seed train and a formerly manually designed seed train?

Simulated and experimental time courses of viable cell density are shown in Fig. 4 for the AGE1.HNAAT cell line for:

• seed train conducted based on passaging at the points in time calculated using maximum and minimum Space-Time-Yield (STY) (= triangles) as well as the corresponding simulation (= line)

• seed train conducted according to the manually designed procedure (= rhombs).

The seed train steps are: 1. culture tube (0.01 L), 2. shake flask (0.035 L), 3: shake flask

(0.13 L), 4. Vario 1000 (0.35 L), 5. VSF 2000 (1 L), 6. Labfors 5 Cell (2.5 L)

For the tool-designed seed train, Fig. 4 shows a good compliance of simulation and experiment (comparison of triangles and line)

The courses of viable cell density show no differences among the tool-designed and the manually designed seed train (comparison of triangles, line and rhombs). This shows that the tool was able to yield an accurate seed train layout only on the basis of two previously performed batches (for parameter identification) and the underlying model.

The tool achieves seed train modeling and thereby enables seed train analysis and optimization as well as the design of new seed trains.

The tool’s advices for seed train design could be realized in lab scale cultivations.

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