High-Throughput Multi-Product Liquid Chromatography for Characterization of Monoclonal Antibodies - An approach to biopharmaceutical development that combines Quality by Design with a suite of visual


High-Throughput Multi-Product Liquid Chromatography for Characterization of Monoclonal Antibodies
An approach to biopharmaceutical development that combines Quality by Design with a suite of visual informatics tools to reduce scale-up risks.

BioPharm International


The glycosylation of biopharmaceuticals produced in mammalian expression systems is profoundly affected by the physicochemical, nutritional, and mechanical environment in the cell culture. This environment changes as bioreactors of different size and configuration are used during drug scale-up. A major cause of altered glycosylation in traditional scale-up schemes is that the large-scale tanks cannot be forced to behave like the small ones used to produce reference material. Scale-down systems overcome this problem by reversing the direction of mimicry and forcing small reactors to behave like the large bioreactors.7

In this step, scale-down bioreactors are used within the QbD framework to select a suitable clone, define the design and control spaces for drug manufacturing, and define and reproduce the cell culture conditions that will be used in the full-scale system and all intermediate-scale bioreactors.

There are some important things to note:

1. Clone selection should be performed with the glycoform pattern of the QTPP in mind. In particular, the selection criteria should include both drug titer and the quality of the GCQAs.

2. Sensitive glycoprofiling methods that measure the most highly ranked GCQAs are needed to support clone selection and experiments to determine the design space (DS).

3. There are many cell culture parameters that could affect glycosylation. These include dissolved oxygen, pH, temperature, ammonia, cell age, the extent of the harvesting period, nutrients, and the use of serum or serum-free media.8,9

4. The A-MAb project shows one approach for determining a DS that includes glycosylation parameters.2

5. Full factorial design of experiments (DoE) for optimizing the DS can be very expensive and time-consuming. This work could be reduced considerably by moving from conventional (e.g., 1 or 3 L) scale-down bioreactors to high-throughput micro-bioreactors that allow multifactorial experiments on hundreds of cultures in parallel.7

6. The results of the A-MAb project suggest that the early promises of expanded QbD DS, and therefore greater leeway for alterations in the product after manufacturing changes, may not be realized. What QbD does deliver, however, is a well-characterized manufacturing process for a well-characterized drug.

The prize for all this work should be a thorough understanding of how to manufacture your drug with consistent and optimized glycosylation at all scales.


Controlled scale-up, with maintenance of uniform drug glycosylation, should now be straightforward. From the QbD perspective, comparability after scale-up is demonstrated with evidence that the control space of your scaled-up process is in your predefined DS.

Scale-up to the full-scale bioreactor would typically be done in stages. At each stage, personnel would invoke ICH Q5E and Q8 (plus annex) to demonstrate comparability and focus on showing consistency with respect to the full set of GCQAs identified for the drug. If the drug has optimized glycosylation and is composed mainly of SM and SH glycoforms, then the analyses are generally straightforward as pesonnel will be searching for the high-abundance glycan species to show consistent efficacy. However, scientists still need glycoprofiling tools powerful enough to show the presence of low abundance nonsafe (N) glycosylation. Analyses of nonoptimized low-activity (L) glycoform dominated drugs need to be more rigorous because even small changes in the proportions of M and H type glycoforms can significantly alter the product's in vivo efficacy.

As part of continuous improvement and lifecycle management, the details of tracked GCQAs should continually be reviewed as new information emerges. This will involve updating SE profiles and impact maps. In particular, companies should aim to gain information to eliminate Type U GCQAs, re-assigning them to either Type I GCQAs or non-GCQA status.

Scientists also should update their set of glycoprofiling tools as new analytical instruments and glycoanalysis methods emerge. These measures will help mitigate the clinical and commercial risks of inconsistent drug glycosylation.

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