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

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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


GCQA Tool # 2: Impact Maps


Figure 2. A partial impact map for monoclonal glycosylation. The diagram shows part of the impact map for core Fucα1,6 and bisecting GIcNAc residues of an antibody relying on ADCC activity for its therapeutic effect. The map shows the degree of effects of glycosylation features on ADCC activity and key evidence for the effects. For this drug, core Fucα1,6 is an efficacy GCQA as there is a critical path (high effects) from Fuc to ADCC to efficacy, but bisecting GIcNAc is not. Note that biGIcNAc could still be a GCQA if it had an impact on another biological behavior of the drug that affected safety or efficacy.
The GCQAs identified in SE profiles are further analyzed and prioritized using impact maps. These are mathematical graphs showing (a) the effects (i.e., the degree and type of influence) of a drug's glycosylation attributes on its biological behaviors, (b) the effects of those behaviors on the clinical safety and efficacy profiles, and (c) evidence for those effects. The effects are represented by lines linking attributes to behaviors and then to safety or efficacy, the degree of impact being denoted by line thickness. Figure 2 shows an impact map. The maps are annotated with references to evidence for the effects. These references are weighted with knowledge strength. This is an index composed of a veracity index (v1 = idea with no experimental evidence, v2 = expert opinion, v3 = peer reviewed data, v4 = data on the drug) and a relevance index (r1 = data for another molecule, r2 = data for a related molecule, r3 = data for your drug). The 12 combinations of v and r can be classified into five groups of knowledge strength from very weak (v1–r1) through weak, medium, strong, to very strong (v4–r3). The knowledge strength can be denoted by the color of impact lines to allow focus on the most reliable data. Interactions between glycosylation attributes or behaviors also can be denoted on impact maps as can evidence for lack of effects.

GCQAs are identified in the maps by critical paths with high effects linking glycosylation parameters by biological behavior to safety or efficacy. GCQAs are prioritized by ranking all behaviors affecting safety in descending order of the degree of impact, doing the same for behaviors affecting efficacy, and then ranking the GCQAs alongside those behaviors.

Impact maps are simple but powerful visual tools to reveal the evidence for assigning criticality to any glycosylation parameter. They should be updated as knowledge is acquired throughout the drug's lifecycle. Note that in this system, the criticality of a glycosylation attribute is independent of both its measurement and control during biomanufacturing. A critical attribute remains critical even when it is well controlled and detected, which are the goals of the next steps.


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