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Apr 1, 2012 (Vol. 32, No. 7)

Small-Scale Modeling and Predicting

  • Optimizing production efficiency of a bioprocess to yield high titers of a desired recombinant protein or monoclonal antibody requires great attention to detail. This begins at initial design and testing of a small-scale process right on through scale-up to industrial-scale manufacturing.

    At every step decisions should be based on a thorough understanding and characterization of the process components (cell and media selection), process parameters (affecting product quality and quantity; defining an acceptable range), and the target product (identifying a product profile that ensures maximum safety and efficacy).

    Achieving these goals demands the integration of available technology and analytical tools with a clearly defined quality by design (QbD) strategy, in which quality and reproducibility are built into the process from the outset, using scale-down process design and analysis methods that reliably model the environmental factors affecting cell growth and viability and protein production in a pilot- or large-scale bioreactor.

    At the BioProcess International Europe Conference in Prague later this month, Daryl Fernandes, DPhil, founder and CEO of U.K.-based Ludger, will discuss a range of topics related to drug design, manufacturing, and biopharmaceutical comparability.

    There is no question that building QbD into drug development can be time and labor intensive, the payoff comes in having a well-characterized, well-understood process for producing a drug compound efficiently and reproducibly at small scale that can be reliably and predictably scaled up to manufacture commercial quantities of a comparable drug that is safe and effective and will pass regulatory scrutiny.

  • Key Principles

    Dr. Fernandes will focus on key principles of QbD—determination and prioritization of critical quality attributes (CQAs), development of an optimized quality target product profile (QTPP), design of experiments (DoE), design space, and control spaces. These concepts will be illustrated primarily by case studies for drug glycosylation patterns.

    Glycosylation is a highly important structural feature of biopharmaceuticals, but it can be challenging for both biopharma companies and regulators. It can significantly affect safety and efficacy profiles of glycoprotein therapeutics, but also add greatly to their complexity, heterogeneity, and variability. It is one of the most difficult factors to control in biomanufacturing.

    In particular, it is not uncommon for the glycosylation pattern to change during scale-up, sometimes leading to serious regulatory issues.

    Dr. Fernandes points to the A-MAb case study, prepared by a consortium of companies known as the CMC-Biotech Working Group, as a good example of what can be learned from the implementation of QbD and of the importance of glycosylation factors. Nearly half of the potential CQAs used to model the design space in the A-MAb study were glycosylation parameters.

    “A-MAb was a superb study. It showed how QbD can help drug companies establish well-controlled, well-characterized biomanufacturing processes. But it also revealed some difficulties with implementing the principles in ICH Q8/Q9. These include the high cost of DoE studies, the troubles when using FMEA to determine and prioritize potential CQAs, and the feasibility of obtaining an expanded design space,” says Dr. Fernandes.

    The mistake made most often in applying QbD to glycoprotein therapeutics has been “starting expensive, time-consuming DoE with an un-optimized QTPP,” he says. Instead, he recommends that companies “invest the time to develop a well-thought-out QTPP with structural features, including glycosylation patterns, designed to optimize the drug’s clinical performance.”

    One can then select an expression system and parameters to deliver a therapeutic that matches that QTPP as closely as possible.

    Controversially, Dr. Fernandes argues that an ICH-type design space is not an essential part of QbD, and that a carefully designed QTPP together with a conventional biomanufacturing process control system should be considered as a first level of QbD implementation.

    However, he also emphasizes that using design and control spaces could significantly improve the consistency of batches of glycoprotein therapeutics and that following a QTPP with optimized, simplified glycosylation can reduce the DoE work needed to develop these.

    Developing an optimized QTPP together with design and control spaces is particularly useful for reducing the problems of altered glycosylation during scale-up. Instead of taking the traditional route to scale-up and designing and characterizing a process at small scale and then assuming that by monitoring and controlling those defined parameters through scale-up the product will not change substantially, he emphasizes another approach. He cites the importance of devoting the time and resources necessary at the DoE stage to understanding what the environment around each cell will be like in a large-scale bioreactor.

    Companies should then use a scale-down modeling approach to vary and mimic those conditions at small scale, defining the optimal design space and acceptable ranges for key CQAs, and use that model as the basis for scale-up. With this strategy, scale-up should yield a more consistent cellular environment and be more predictive of what will take place in the bioreactor as process scale increases.

    “The value of QbD comes in advancing your understanding of your drug and characterizing your process, not necessarily in having an expanded design space,” says Dr. Fernandes. For glycosylation, optimizing the glycoform population of a drug by increasing the percentage of high-activity isoforms can enhance its safety and efficacy profiles and simplify the drug profile, making it easier to scale up.

    Dr. Fernandes adds, “One of the biggest advantages of QbD is in bringing people together early on in a drug development project, from across departments and disciplines, to develop a shared model for systematic realization of that drug—i.e., what it should do for the patients, how it should work in vivo, what it should look like (structurally, at the molecular level), how it should be characterized, and how it could be manufactured.”

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