Guest Commentary: Go parallel in a big way

The challenges for the industrial production of any medicinal product are substantial. For biologics in particular, the process defines the product. As with any process, a series of units of operation must be completed, controlled and analyzed; for each unit of operation, a number of steps exist, and at each one of these steps a number of possible variables may be present which impact on the critical quality attributes of the product and therefore on its clinical safety and efficacy.

 

Approaches that build quality into a process, through the use of quality by design, are favored by the regulatory agencies.¹ However, the number of experiments required to build quality in to a process can be vast; in theory, it should be possible to test all the possible combinations of variables for a given process, but in practice such undertakings become too large (and expensive) to be performed. Currently, the most widely used approach to circumvent this problem is the design of experiment (DOE) approach. DOE is a statistical-based tool which reduces the number of experiments needed to model the responses of multifactorial experiments by testing a subset of values for each variable and using these values to predict the impact of changes to one variable on other variables and the product.

 

Ultimately, however, the DOE approach is still limited by the throughput of the experimental method available to analyze the outputs, and it doesn’t test all possible combinations of variables. The amount of experimentation to arrive at this should not be underestimated since the challenge for process optimization lies in the combinatorial nature of the problem—how can multiple variables be tested in parallel?

 

The concept of combinatorial cell culture² was developed to tackle the problem of multiple variables in a high-throughput format and was initially applied to the paradigm of stem cell differentiation.³ As with any process, the differentiation of stem cells (both in vivo and in vitro) is determined by the concerted action of a series of combinations of cell-signaling molecules acting upon the cell at specific times in a specific order. As there are multiple steps in the differentiation process and multiple possible variables (ligands, cytokines, growth factors, etc.) at each stage, the number of possible combinations can be vast. For example, if a process has four stages, and at each stage there are 10 possible variables, a total of 10,000 variable combinations are available. Testing 10,000 combinations in parallel using standard methods (even in a miniaturized form) is extremely time-consuming, expensive and difficult to analyze.

 

An example of this level of parallelization, in a format that is easy to handle without the need for specialized equipment or large expensive experiments, is Plasticell’s CombiCult^2,3 product, a high-throughput platform that uses combinatorial cell culture technology to screen tens of thousands of protocols in one experiment. It combines miniaturization of cell culture on microcarriers, a pooling/splitting protocol and a unique tagging system to allow multiplexing of experiments.

 

Using a process like this, cells grown on microcarrier beads can be shuffled randomly, stepwise, through multiple conditions using a split-pool method. The iterative process of splitting, culturing and pooling can sample all possible combinations of conditions in a predetermined matrix. If X number of conditions are tested on each of Y number of cycles, X^Y protocols are tested simultaneously. Each condition can be coded with a unique fluorescent tag that attaches to the beads. At the end of the process, beads bearing cells with the desired phenotype are identified by a screening assay (e.g. immunostaining or reporter gene expression and individual positive beads are isolated). The cell culture history of each positive bead is then deduced by analysis of the fluorescent tags attached to the bead. Bioinformatics is then used to analyze and rank the data.

 

How can such technology be applied to the upstream process of protein or antibody production?

 

The efficient and economical production of therapeutic proteins, antibodies or other biologics depends on the ability to create cell lines that can synthesize and secrete the protein of interest in high amounts over a reasonable time frame using processes and materials that are inexpensive and easy to reproduce. The mammalian expression systems most widely used are CHO cells and hybridomas. These cells are usually genetically engineered to overexpress the protein of interest and to secrete this into the media. Years of research have improved dramatically the yields of proteins from mammalian systems, but there is still room for further enhancement. Optimization of the genetic manipulation of the cells is outside the scope of the technology at present; nevertheless, clones produced in different ways may be tested simultaneously for other variables.

 

The first stage in a screen is to define the desired cell phenotype and develop an assay to detect it. The most desirable phenotypic characteristic of the cell may be the protein yield—for example, protein secretion can be easily detected using FLSS (fluorescent labeling in semi-solid media⁴) if an appropriate antibody for the protein of choice is available. Using an automatic clone picking instrument such as Molecular Devices’ ClonePix, beads bearing high-producing cells can be automatically detected by the extent of the halo they produce in the semisolid media containing a fluorescent antibody against the secreted protein. Further cellular phenotypes can be additionally assessed and detected using immunostaining with other fluorophores.

 

Once an assay has been established, a matrix with the variables to be tested can be designed. For example, cell culture media supplements such as butyrate are known to increase the yield of product.⁵ Even though the total number of combinations can be large (10,000 to 100,000), there are some practical considerations in defining the number of stages to test and the number of conditions to be tested at each stage (e.g. 10 conditions over four stages = 10,000 possible combinations).

 

Each condition can be either a simple media composition with one or two components or a complex one containing a number of small molecules/growth factors/lipids and hormones. Each condition is individually marked with a unique fluorescent tag which will allow its identification. The cells can be incubated in these conditions for a predetermined period of time and then subsequent rounds of pooling and splitting can be carried out. During each stage another set of variables can be tested—these can be other media conditions, supplements or physical parameters such as pH, temperature and oxygen tension.

 

At the end of the culture period, each pool can be either fixed and immunostained, or plated in semisolid media containing a fluorescently conjugated antibody against the expressed protein. After three to five days, halos of antibody-antigen precipitates can be visualized under epifluorescence, beads can be picked and then the associated tags can be analyzed and their culture history can be deduced. This data is fed into specialized software which uses clustering algorithms and probability analysis to rank the most successful combination of variables that give the desired outcome. Depending on the number of positive beads and the degree of clustering, a set of 10 to 20 possible pathways (or sets of combinations) can then be validated, thus reducing the total number of combinations of variables from thousands to just a handful. Mapping the journey taken by positive beads will elucidate a combination of process steps that best produces the desired cell phenotype, providing the basis for the subsequent step of process refinement to define the final protocol.

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Diana Hernandez, Ph.D., is a principal scientist at Plasticell Ltd. She is a molecular and cellular biologist with more than 20 years experience in the field of stem cells. Prior to working at Plasticell, she held research posts at University College London and Imperial College London.