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Nov 1, 2011 (Vol. 31, No. 19)

Getting the Most from Process Operations

A Strong, Predictive Expression System Is Essential to Maximize Productivity

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    A combination of technologies such as the XD process and a robust cell line in PER.C6 enable scientists at Percivia to achieve high cell densities and high product yields.

    Much has been written about volumetric productivity for therapeutic protein manufacturing processes. While focusing on titer alone ignores such factors as time, facility utilization, and product quality, a good deal of basic research continues on what makes production cells tick.

    Maximizing productivity begins with a strong, predictive expression system, followed by a long development period where cells and reactor conditions are optimized. Yet what factors result in a “high producer” or “low producer”?

    Expression profiling using CHO-specific microarrays has failed to uncover the answers, which must lie with varying contributions from transcription and translation rates, protein folding, and assembly. “But which combination leads higher production has been very difficult to unravel. We’re no closer to answers than we were a few years ago,” admits Mark Melville, Ph.D., director at Percivia.

    He is also skeptical about cell-line engineering efforts to date, which he says have not “done a lot to push the envelope for protein expression.”

    Yet basic research on expression strategies continues. Process developers purposely mimic codons in genes occurring naturally in the host system without direct data to support that choice, according to Mark Welch, Ph.D., director of gene design at DNA2.0.

    This is known as codon bias: an organism’s preference for one synonymous codon over another one. Codon utilization is directly related to protein expression—not all equivalent sequences generate proteins equally. But codons purposely “biased” to accommodate the host organism do not always work best.

    DNA2.0 has devised a computational platform that “surveys” the gene of interest for codon variations that produce the highest protein expression—between 10 and 100 times as much as competing services, the company claims.

    “There are many ways to search for gene variants,” Dr. Welch continues. “Ours is more systematic, providing more information that covers a wider range of variables, and we can do it with fewer clones.” That means considerably fewer productivity assays.

  • Clone Selection

    Once the gene is inside, it’s time to select the cells that express the highest protein levels at the highest quality. Clone selection resembles high-throughput screening of small molecule drugs in its reliance on the law of large numbers.

    Mark Melville points to tools like Clonepix systems from Genetix, which uses fluorescence imaging to identify colonies growing in a semi-solid matrix within a Petri dish. The technique begins with ultra-high dilution that essentially separates cells individually, and ends with a liquid handler sucking up the colonies and transferring them to microtiter plates.

    Melville also likes flow cytometry, another technique that relies heavily on robotic transfer of single cells to plates. Due to the low viability of isolated cells, process developers often employ tens of thousands of wells, and the technique is expensive in its acquisition and ongoing operation.

    “But there have been some recent advances that incorporate omics approaches through system biology, which I expect will yield fruit within the next few years.”

  • Nature vs. Nurture

    Despite strides in productivity, biomanufacturers remain committed to even higher titers. “I don’t think we’ve reached the ceiling yet,” says Hitto Kaufmann, Ph.D., vp of process science at Boehringer-Ingelheim.

    Before process development even begins, companies need to gain a better understanding of how molecular attributes affect expression. Boehringer-Ingelheim is developing sophisticated analytics to screen for “expressability” within a bioreactor. Miniaturized (scale-down) systems are the key, Dr. Kaufmann says.

    The question of nature or nurture—innate cellular capabilities versus growth conditions—always arises in these discussions.

    Many cell culture experts believe that media and feed strategies have been most responsible for the run-up in volumetric productivity, and Dr. Kaufmann believes that significant, further improvements are coming. Improvement depends on techniques for rapidly screening these conditions and tailoring them to specific cell lines.

    Melville believes the relative contributions of nature and nurture are equal. “You can’t separate cell line from process; they are part of the same continuum. You design the process to fit the cell line, but you also select the cell line to fit the process.”

    On the “nature” side, targeted integration of genes into the CHO genome has become the new “City of Gold.” The driver here is not volumetric productivity, but faster development. “CHO cells are prone to genetic instability,” Dr. Kaufmann says. “To have designed loci within the genome that ensure stable, high expression will be a key area of focus for biomanufacturers. If successful, this approach may allow developers to skip screening for clones and media.”

    Melville disagrees. “I don’t think I’ve seen any site-directed integration that has yielded higher expressions than a good cell-line screening program.” He describes a “good” screen as a reliable model that assures what occurs during clone selection will be relevant throughout process development and beyond.

    “Stable integration sites and site integration have come a long way in providing a reliable, minimal expression level, but they’ve never been able to achieve ten grams per liter or higher, which we’re seeing in other systems nowadays.”

    Process conditions can affect a protein product’s quality and help fine-tune its in vivo behavior—characteristics that do not translate directly into “grams per liter,” but are just as significant.

    Post-translational modifications (PTMs) can affect any number of properties. Developers of therapeutic proteins have only begun to leverage knowledge of the more than 100 PTMs, which in some cases are fine-tunable by merely altering process conditions like pH or temperature.

    We know that fucosylation affects the activity of antitumor monoclonal antibodies—lower levels of this sugar increase therapeutic efficacy. Similarly, higher sialylation results in a longer circulating half-life.

    “The more we understand relationships between process conditions, glycosylation, and physiologic function, the more companies will exploit these strategies,” Dr. Kaufmann explains. “A few of these functional relationships are known, but many more remain mysterious, at least to the degree that we can be certain of in vivo relevance. That will change.”

    In the future, companies may develop several CHO cell lines, each producing unique glycosylation patterns that tune in desired characteristics. Manufacturers of biosimilars may be able to exploit PTMs to differentiate their products as well.

    The “food chain” for deploying such technology is typical for biotech. Early work and proof of concept in cells typically occurs at universities. Smaller biotechnology firms will license and optimize the relevant technologies.

    If successful, they will be acquired by a much larger firm. For example GlyCart was formed in 2000 as a spinoff of the Swiss Federal Institute of Technology, Zurich. Roche acquired the company in 2005 for, among other things, GlycoMab®, a technique for directing and optimizing glycosylation in antibodies.

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