February 15, 2014 (Vol. 34, No. 4)

Lukas Flueck-Kabay, Ph.D. Scientific Account Manager Genedata
Hans Peter Fischer, Ph.D. Head Genedata
Maria Wendt, Ph.D. Senior Scientific Consultant Genedata

Integrated Workflow Support and Data Platform for Systematic Decision-Making

In recent years, many advances have been achieved toward robust biopharma production. Nevertheless, despite technological progress, a universal production system does not exist. And, due to the countless parameters involved, such a system may never be available.

Therefore, there is an urgent need for tools that help rapidly develop better production systems. This tutorial will examine select steps in a typical biologics development process and show how integrated data analysis can help guide this process from small-scale transient expression to large-scale product manufacturing.

The presented data platform, Genedata Biologics™, is enterprise software designed for biotherapeutics R&D. The technology, which has been built over recent years in collaboration with major pharma and biotech partners, supports the full process from the early discovery phases (e.g., antibody screening) to late-stage candidate development (e.g., upscaling).

The following discussion will focus on the platform’s use in cell-line and bioprocess development.

Several entities such as host cell lines, vectors, promoters, and selection systems, intrinsic product characteristics, and bioprocess methods and conditions, have a significant effect on final productivity. The initial challenge is to identify an appropriate expression system and design optimized vectors to generate cell lines expressing the product molecule in sufficient quantities.

For biopharmaceutical applications, host cell lines must support high product expression over an extended time while demonstrating scalability at a high viable cell density and maintaining genetic stability.

In addition, for many biotherapeutics, correct post-translational modifications (PTMs) are crucial. Cell lines such as chinese hamster ovary (CHO) and human embryonic kidney (HEK) are suitable systems as these cells have the ability for human-like PTMs (e.g., glycosylations).

The expression of proteins in these cell lines requires the design of appropriate expression vectors. They should allow for expression levels independent from the site of integration in the host cell genome and maintain the expression productivity over time.

Two key elements in the optimization of an expression vector include the selection of promoters/enhancers (e.g., CMV, EF1-α) to drive and enhance the expression of the transgene, and sequence elements to stabilize and enhance the translation of the transcript (e.g., Kozak).

In addition, appropriate leader signal sequences and codon optimization can further enhance secretion and transcription, typically resulting in higher productivity.

The Biologics platform provides tools to automate the design of new constructs and support the subsequent DNA synthesis workflow (Figure 1). The platform reduces the number of repetitive, error-prone in silico cloning actions and manual handling of sequence information. It automatically identifies, annotates, and registers both DNA and proteins.

Once DNA has been synthesized, it confirms the correctness of the vector sequences against the construct designs.

During evaluation of different expression systems in test campaigns, all experimental results are captured and tracked, together with transfection and expression protocols and relevant analytics data. As all results are stored in a single system, this dataset can be used for effectively identifying the best expression system, e.g., defined by analyzing correlations between productivity and host cell line-vector combinations.

Figure 1. Vector optimization: The Biologics platform automates the in silico generation of large vector panels for investigating combinatorial vector control elements. Each resulting vector is fully annotated and registered along with the encoded therapeutic antibody or protein. These vectors are then used for validating sequences of synthesized DNA against the construct design.

Cell-Line Development

In the next step, stable cell lines are established based on the initially selected expression system. The goal is to obtain monoclonal, stably expressing cells that produce the desired molecule in sufficient quantities. After the initial creation of a cell clone pool with selection for producers and growth recovery, the production yield can be further multiplied using a selection system leading to gene amplification (e.g., MTX).

Finally, cell pools are screened for the highest producers using high-throughput automated clone isolation resulting in monoclonal cell lines.

Cell-line generation is a lengthy and costly process. It requires a high level of automation resulting in large numbers of material batches including cell lines, and expression and purification samples. To systematically improve cell-line properties, it is critical to track full cell line ancestries and resulting expression titers (Figure 2), cell growth and cell viability data, among others.

Effectively managing cell-line development data enables faster selection of superior monoclonal cell lines that can be handed over to process development and up-scaling groups.

The Biologics platform enables tracking and sharing of each cell line’s development history, including vector, host, passage, and expression data, which is of critical importance for downstream groups.

Figure 2. Cell-line development (CLD): Antibody titers from derived cell lines are colored according to parental clone pool. The automated capture, visualization, and analysis of clone hierarchy and productivity data within the Biologics platform facilitate fully automated high-throughput cell-line development and faster decision-making.

Bioprocess Optimization

Once a high-producing, stable cell line is available, the respective bioprocess needs to be optimized to further increase product yields and quality. The exploratory processes applied during cell-line development must be transferred to manufacturing scales.

Moreover, cell lines must be adapted to suspension cultures and to serum-free (e.g. animal-component free) or chemically defined, optimized media (CDACF, SFM, PFM). These are challenging activities that have a profound effect on productivity.

Typically, the optimization process defines a fed-batch process that is based on a basal medium supporting initial growth and production, and a growth medium providing optimized supplementation of nutrients. In addition, process parameter settings such as feeding strategy, temperature, pO2, pH must be systematically evaluated.

During the culture adaptation of the cells, metabolic parameters (e.g., consumption rate, lactose), cell growth and viability, as well as product titers are monitored for different production volumes, mixing times, O2 transfer, agitation rates, cell densities and others.

Given the large sets of variables in a process optimization campaign, there is a strong need for an integrative data-management solution to track and analyze all relevant data (Figure 3). An integrated biologics platform provides flexible storage for all bioprocess development data including assay and analytics results (e.g. ELISA, Biacore, FACS, SDS-PAGE, SEC, DLS, MS).

Powerful visualization and analysis functionalities enable researchers to closely monitor the optimization process and to select best process parameters—all of which help to direct and guide the optimization process in a structured and organized mode, which saves valuable resources and time.

A scalable workflow, data management, and analysis platform for tracking, sharing and analyzing all data is required to effectively cope with and harness information from the volume of data and complexity of today’s bioprocess development activities. This system, which eliminates potential errors during DNA synthesis, construct design and subsequent protein production, has resulted in significant cost savings.

By supporting timely capture and evaluation of cell-line development data, it is possible to employ full automation, expand panels of cell-line evaluations, and consider earlier cell-line development work already during the research phase.

A data-driven approach during media optimization and upscaling production minimized costs by facilitating timely, strategic decision-making and guiding process development.

Figure 3. Media optimization and upscaling: Availability of integrated data (that is, productivity, expression system health, process condition, metabolic, etc.) as shown facilitates prediction of system behavior and provides a more systematic means of evaluating and determining the best course of process optimization during process development.

Lukas Flueck-Kabay, Ph.D., is scientific account manager, Maria Wendt, Ph.D., is senior scientific consultant, and Hans Peter Fischer, Ph.D. (hans-peter.fischer@genedata.com), is head of the biologics business unit at Genedata.