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Mar 12, 2014

Implementing a Full Quality-by-Design Strategy

Luxury or necessity for developing the best biologicals?

Implementing a Full Quality-by-Design Strategy

Scientist using Umetrics' MVDA analysis software.

  • “There is the perception in biopharm companies that it is too expensive and takes too much time to implement full QbD (Quality by Design) for biologicals,” states Professor Jose Menezes, Ph.D., Institute of Biotechnology and Bioengineering, Portugal. Dr. Menezes speaking at the recent PAT and QbD Forum 2014 at the Sartorius College in Goettingen, Germany adds, “Without considering the benefits over a product’s lifecycle the cost would hardly be justifiable. However, if you take into account the product’s lifecycle, you’ll see increased process and product knowledge which will mean less batches will be lost, and a more reliable process that is less likely to have quality and recall issues so in the longer term your drug substance/product will actually be less expensive to produce.”

    Mario Becker, director, PAT & automation at Sartorius Stedim Biotech adds, “Key factors driving the implementation of PAT and QbD in the biopharmaceutical industry are economic,  particularly those concerning the optimization of both new and existing processes to deliver higher yields and titers, and time to market. These involve accelerating process development times and lowering the associated R and D costs.”

    QbD in the biopharm industry is a systematic approach to developing a drug or vaccine and its key elements are defining a target product profile (TPP), identifying the critical quality attributes (CQAs), and then defining a manufacturing process to ensure the CQAs meet the TPP. This all sounds quite logical and straightforward but as many of the speakers at the forum noted it is often anything but this.

  • Model and Measure

    “With QbD of biologicals you have to do a risk analysis to identify by what range you can vary upstream and downstream bioprocess conditions without directly affecting the CQA,” Anurag Rathore, Ph.D., professor in the department of chemical engineering at the Indian Institute of Technology Delhi, explains. “Then you can do your Design of Experiments (DoE) but for a full factorial run of 10 factors that might affect CQA you are looking at running 1,000 experiments and often there are more than 10 factors that can affect quality. For most companies this is just not feasible and is where using high-throughput process development models and multivariate data analysis (MVDA) on a continuous basis is essential.”

    Petter Moree, director, global product management at MKS Umetrics agrees, adding, “Scientists can generate upstream DoE bioprocess run information using an automated microbioreactors system, and this can be plugged into their MVDA analysis software to provide a process understanding to determine which parameters affect the CQA of their biologics. However, this does mean investing time in data analysis when it is collected rather than waiting until the QC people have analyzed it because by then they might have lost batches.” According to Moree, Lonza has presented implementations of scaledown models and real-time MVDA with its media development and by removing unwanted variation in one of their bioprocesses has increased the yield of a biological by 75%, decreased cycle time by 40%, and increased throughput by threefold.

    One company that is embracing the real-time measurement philosophy in its upstream bioprocess development is GSK. Andrew Heinrich, senior scientist, biopharm process research states: “We are generating domain antibodies and have to find good scalable methods of measuring cell density online.” Heinrich presented a case study using a cytokine product expressed in CHO cells, which were cultured in shake flasks, 2 L and 50 L bioreactors. Cell density was measured using an Aber instruments biomass probe and offline using a ViCell cell counter and a Trypan blue assay. The Aber probe measures capacitance and because it scans every 30 seconds Heinrich’s team used SIMCA MVDA software to analyze the large amount of data it produces. “The Aber probe shows good correlation with the offline cell density measurements in 50 L and 2 L bioreactor studies,” says Heinrich. “Realistically it is not practical to use the Aber probe with 100 shake flasks but it can be used with benchtop bioreactors. We are now moving towards single-use technology much more and are using the ambr 250 automated microbioreactor workstation as a workhorse with the Aber probe for cell screening as this means we can use our online measurements as feedback control to increase our in-process knowledge.”

  • Full Fat QbD

    For companies that want to perform a full QbD for biologics production, then according to Dr. Menezes they have to do an end-to-end analysis, which involves analyzing the raw material of their media batches and their cell banks, as well as the upstream and downstream bioprocess conditions, and doing this during development and at specific points in time during routine manufacturing (the product lifecycle). This can help predict the impact of defined sources of variability on their product quality and help improve consistency through increased process knowledge.

    “QbD is helping us to realize that you have to take a systems’ view and that the current QbD formulation is suboptimal, if as expected the different systems’ components interact with each other,” Dr. Menezes says. “The current QbD formulation is not equivalent to the design-for-six-sigma approach that can really ensure an overall optimal solution in terms of product consistency over its lifecycle.”

    This concept that putting together optimized stages may result in an unknown impact on CQA was demonstrated by Fai Poon, Ph.D., director of cell culture at Hisun Pharmaceuticals. “We develop biosimilars and in 2011 we wanted to optimize the titer of our first biosimilar so we worked with different feeds and found we could improve our titer by six fold,” Dr. Poon explains. “But by optimizing the culture media supplement we decreased the sialic acid content of our media by 20%, and this meant the antibody yield decreased because it lead to low yield purification so it had a major impact on the CQA of our biosimilar product.”

    To overcome this problem, Dr. Poon and his team looked carefully at the media components with the aim of improving the sialic acid content by 20% while still retaining their titer increase. They analyze the components of four feeds all based on the commercial feeds: HyClone™ Cell Boost 5™ Supplement; Irvine Scientific BalanCD™ CHO Feed 3; Life Technologies CD EfficientFeed™ C AGT™ Nutrient Supplement and SAFC® EX-CELL® CD Hydrolysate Fusion. They then performed a Principle Component Analysis (PCA) using SIMCA MVDA software, and this identified six significant components that are critical to the sialic acid content. They then added the six components to their media and performed small-scale spin tube experiments with their antibody clones to determine which components would increase titer and sialic acid content. Two components increased sialic acid content by greater than 20%, and these were then used in scale-up work at 2 L, 30 L, and 1,500 L to show that the sialic acid and titer increases were reproducible at large scale. Dr. Poon concludes: “Titer improvement can sometimes lead to changes in CQA. This is where MVDA is useful to pinpoint which media components are important and is a potentially important media development tool.”

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