Getting the most out of producing therapeutic biologics

Pharmaceutical Technology Europe

Pharmaceutical Technology Europe, Pharmaceutical Technology Europe-05-01-2008, Volume 20, Issue 5

The market for monoclonal antibody therapeutics was approximately $24 billion (€15.2 billion) in 2007 with an estimated growth potential of up to $47 billion (€29.8 billion) by 2012.1 There are 21 marketed antibody products, five of which continue to dominate the market (Avastin [Genentech], Herceptin [Genentech], Humira [Abbott Laboratories], Remicade [Centocor Pharmaceuticals] and Rituxan [Genentech]) and collectively account for approximately 80% of the market share. There has been a notable swing towards biologics in pharma and biotech company development pipelines via a shift in strategic development or acquisitions. One of the more notable acquisitions in this area is the incorporation of Cambridge Antibody Technology (CAT), now MedImmune, by AstraZeneca in a deal worth £567 million (€718.3 million) in 2006. Monoclonal antibodies now represent the strongest growth area in the therapeutic proteins market sector. By 2009, it is forecast that they will account for 48% of all sales of therapeutic proteins.

The increased demand for therapeutic antibody products per se, coupled with the need to administer most of these molecules at relatively high doses, translates into a manufacturing requirement for possibly hundreds of kilos of product collectively per year. Accordingly, optimizing the production efficiency of these molecules is an area of intense research within the CMO and biopharmaceutical community. This is particularly so where the cost of goods weighs heavily in the business models of innovator companies seeking to develop new molecules for less common clinical indications and the emerging sector for 'biogeneric' and 'follow-on' biologics.

One response to the demand is a shift to larger bioreactor size with, in some instances, bioreactors of 20000 L working volume. There has also been a move from continuous process operations to fed-batch processes, and an area of increasing interest is the optimization of the expression cell line itself. Here, there is focus on identifying and developing ways in which the genes for the protein of interest can be best incorporated into the host cell to give stable and consistent high levels of secreted and active protein. It is recognized that optimized high titre expression clones have significant commercial benefits dictating bioreactor size and the overall manufacturing cost of the therapeutic.

Maximizing biologics expression in mammalian cell systems

The vast majority (~60–70%) of antibody and recombinant protein molecules are produced in mammalian cells.2 This reflects the basic fact that most biologics are glycoproteins and require at least a mammalian pattern of glycosylation to achieve satisfactory clinical efficacy. Mammalian expression cell lines produce an acceptable glycosylation profile so cell lines such as Chinese Hamster ovary (CHO) have traditionally been used to produce the majority of protein therapeutics. Other mammalian cells used include mouse SP2/0 and, more recently, proprietary human retina-derived cells PER.C6. Further development of the CHO cell line via the introduction of human glucosaminyltransferase and fucosyltransferase genes aims to produce a more human-like pattern of glycosylation. Such 'glyco engineering' strategies are also being applied to nonmammalian host cells, such as yeast. It remains to be seen whether these and other nonrodent cell production platforms, including plant cells, transgenic animals and transgenic plant production, will begin to replace traditional platforms.

The pattern of glycosylation is particularly important regarding biogeneric molecules where 'bioequivalence' and being able to demonstrate identical or near similarity to a previously marketed reference biologic are critical to obtaining market approval. Technological developments such as the lectin array technology of Procognia (Israel) may surpass existing HPLC-based methodology in being able to rapidly monitor and define the glycochemistry of biogeneric and other glycoproteins during the development and production cycle.

Unlike bacterial expression systems, mammalian cell culture systems have a number of constraints such as relatively low protein yield and a lengthy development time required to select stable expression clones with optimal manufacturing profiles. To overcome these limitations, companies have placed a priority on optimizing the cellular expression systems used to produce the therapeutic. For example, Boehringer Ingelheim (Germany) through its BI HEX process has optimized its approach to working with CHO cells to give high titre expression in animal component-free media, and can claim productivity of >50 pg per cell per day (pcd) and in excess of 6 g/L in fed-batch processes for antibody production.

Enabling the cell line to grow at a higher density in culture is an additional consideration when developing an optimized production protocol. The human cell line PER.C6, developed by Crucell (The Netherlands), can grow to a very high density in culture with high cell specific productivities of >50 pcd in some instances, translating to an excess of 5.8 g/L or even higher in a fed-batch environment. Compared with the traditional CHO system, a human cell line may exhibit more favourable post-translational processing of the therapeutic, but differences in sialic acid group derivatives in human and other mammalian proteins lead to the possibility of host immune cell responses (immunogenicity) even via this route of production.

A need to reduce development time has spurred the application of high-throughput automation to identify cells with the desired productivity and other phenotypic attributes. One example is the ClonePix FL instrument (Genetix Ltd, UK), which identifies and selects desirable clones. Similarly, a system employed at Pfizer (CN, USA), known as the 'workcell' robot, is routinely used to rank and select suitable clones prior to scale-up in bioreactors. In some groups, fluorescence-activated cell sorting machines have also been employed to screen and select relevant clones at the single cell level.

Engineering strategies in mammalian expression systems

Developing a stable, high titre expression cell line is a critical initial step towards obtaining clinical grade material. Genetic engineering approaches to obtaining an optimal expression cell line are based on the introduction of the gene for the desired protein into the chromosomal material of a host cell line. The configuration of the introduced gene, the controlling elements, and other genes required that provide the selective principle, are critical coparticipants in the success of the procedure. These crucial 'ancillary' elements are often proprietary, and subject to manufacturing licences commanding development milestone payments and royalties on eventual sales of the manufactured product.

One example of an innovative genetic engineering approach that exploits a natural aspect of cell physiology is the work of UniTargetingResearch (Norway). Expression vectors are constructed to contain optimized combinations of elements acting on the mRNA of the introduced gene in such a way that it becomes directed to a secretory translation compartment within the cell and boosts cellular0020output of the protein (Figure 1). The UniTargeting approach has been validated in a number of independent laboratories and has shown significant improvements compared with conventional expression systems. Other strategies, such as that employed by ICOS (now CMC-ICOS, Denmark) use a vector containing Chinese Hamster elongation factor 1 alpha (CHEF-1) promoter. This strong constitutive promoter circumnavigates the requirement for gene amplification steps in CHO host cells. Gene amplification is a commonly employed strategy to drive higher levels of expression than would otherwise be achieved from the same gene present at a lower copy number. However, as with many approaches, a balance is required as copy number amplification can be associated with genetic instability and increased cell doubling times. The genetic stability of the clone during the lifetime of the cells in a bioreactor is a critical point, especially in terms of GMP monitoring requirement guidelines. As a point of strategy at Accuro Biologics (UK), expression clone development projects generally avoid the use of multiple rounds of copy number amplification in favour of lower producing, but ultimately stable, cell lines with acceptable doubling times in culture.

Figure 1

A common phenomenon in expression cell lines is for their productivity to drop off with time. Sometimes this can be related to genetic instability, but often it is attributed to 'gene silencing' — an effect believed to be related to the positioning of the introduced gene of interest within the host cell chromosomes. Most regions of the chromosome are inactive and are held in a dormant state. Two broad approaches to overcoming this limitation have been explored. One is to employ elements within or together with the expression vector, which attempt to lock the incoming DNA into an active configuration within the genome of the host. Examples of this approach include the ubiquitous chromatin opening element system of Cobra Biomanufacturing (UK) and the matrix attachment region elements of Selexis (Switzerland). Another approach is to target the incoming gene of interest to integrate within an active part of the genome. Site-specific integration, for example, using genetic systems such as FLP/FRT recombination, is being used to incorporate the expression vector into expression 'hotspots' within adapted host cell lines. The groups at PDL Biopharma (CA, USA) and Regeneron Pharma Inc. (NY, USA) are using this strategy to produce high antibody titres in their production cell lines. Similarly, the teams at Sangamo BioSciences (CA, USA) are developing 'zinc finger nucleases' for targeted gene insertion and cell line development.

On the go...

Downstream processing considerations

The process of optimizing an expression line for transfer to a bioreactor environment is unpredictable and can be time-consuming. A number of factors must be taken into account such as the variation in growth characteristics displayed by individual cultures and the variation in quantity of production (Qp) of cell lines. The Qp level of a given culture is not the single determinant for progression to scale up, as it does not necessarily translate to the highest volumetric titre within the whole culture. The rate of growth, density within culture and production concentration are all key factors when selecting scale up candidates, as is the stability of sustained expression with time.

The limitations of production within a mammalian host cell environment may translate to a 'ceiling' level of production, even in the presence of additional factors that optimize the cellular productivity and growth parameters of the cell line. Physiological levels of secreted antibody production from B-cells in the body are thought to be ~100 pcd in response to a pathogen,3 suggesting a biological ceiling for antibody production in cells. This apparent optimal, in vivo response is compared to the production levels routinely gained in bioreactors of 20–50 pcd for antibody production.2 The expectation of gaining titres of up to 10 g/L in bioreactors may be attainable, although this places pressure on both upstream and downstream development processes, and may ultimately be constrained by the host cell's ability to process and secrete the desired molecule.

Acknowledgements

I gratefully acknowledge the input of G. Carter and A. Hamilton for reviewing the manuscript.

Daniel Ozanne is a Business Alliance Manager at Accuro Biologics Ltd (UK). He is responsible for the global partnering of Accuro's integrated antibody and protein development services with large pharma and biotechnology companies.

References

1. BCC Reports, Global market for monoclonal therapeutics and diagnostic imaging products worth $56 billion by 2012, (2007). www.bccresearch.com

2. D.M. Dinnis and D.C. James, Biotechnol. Bioeng.,91(2), 180–189 (2005).

3. T.D. Randall, R.M. Parkhouse and R.B. Corley, Proc. Natl. Acad. Sci. USA, 89(3), 962–966 (1992).

4. F.M. Wurm, Nat. Biotechnol., 22(11), 1393–1398 (2005).