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


Table 1. Candidate glycosylation critical quality attributes (GCQAs) for a typical monoclonal antibody
The first step in this approach is to establish a comparability system for glycosylation that takes into account the clinical performance of the drug's component glycoforms rather than just glycan primary structures. This is the heart of the QbD framework and is achieved by basing the measurement and control of glycosylation on glycosylation critical quality attributes (GCQAs), i.e., glycosylation parameters that affect patient safety and clinical efficacy. Table 1 shows potential GCQAs for a typical monoclonal antibody (MAb).

Determining critical quality attributes (CQAs) is a crucial part of implementing QbD. ICH Q8 describes failure mode and effects analysis (FMEA) as a tool for determining the criticality of a drug's attributes. However, as discovered in the A-MAb project,2 there are two significant problems with applying FMEA to drug glycosylation. First, the traditional FMEA equation (risk priority number = severity occurrence detection) applies to the detection and occurrence of faults during the manufacturing process. However, the criticality of a therapeutic's structural attributes should depend on its effect on clinical performance, but neither on the influence of the manufacturing process on that attribute nor on the effectiveness of the analytical tools. Second, drug developers using FMEA generally recognize the problems with the standard RPN equation and will modify it, but there is no consensus on what factors should be in the new version and how those factors should be scored. In the case of A-MAb, a MAb for the treatment of non-Hodgkin's Lymphoma, the therapeutic effects of B cell killing occur primarily by antibody-dependent cell-mediated cytotoxicity (ADCC) and are influenced by the drug's Fc glycans. The A-MAb report details the use of two FMEA variants for assessing the criticality of various glycosylation parameters (galactose, afucosylation, sialylation, high mannose, and nonglycosylated heavy chain). The two tools scored criticality in different ways and gave roughly similar results for the risk ranking of the A-MAb glycosylation parameters but showed dissimilar risk levels. For example, tool #1 (where criticality = impact uncertainty) categorized sialylation as high risk while tool #2 (criticality = severity likelihood) scored it as moderate risk.

At Ludger, rather than using FMEA, we use two informatics tools developed for reliable GCQA determination and ranking: safety and efficacy (SE) profiling and impact maps. These tools are used iteratively to build up and refine GCQAs throughout the product lifecycle. They address factors embedded in the A-Mab FMEA tools described above, but deal with them in a different way.

GCQA Tool #1: SE Profiles

Figure 1. The annotations above and to the right of the matrix indicate the key glycosylation features of drug glycoforms occupying the corresponding matrix squares. Each square represents a safety–efficacy category. Potential safety and efficacy glycosylation critical quality attributes (GCQAs) are shown in the rows and columns respectively. A drug with optimized glycosylation is composed of glycoforms from the SM and SH categories (indicated by the squares with thickened borders).
SE profiling is a tool for discovering potential GCQAs. It involves classifying the component glycoforms of a drug into different safety and efficacy (SE) categories and analyzing the structural relationships between glycoforms in those categories. For a simple classification, there are three safety categories: S (safe), N (not safe), and U (unknown safety level) and four efficacy categories: L (low efficacy), M (medium efficacy), H (high efficacy), and V (unknown efficacy). There are 12 combined SE categories for glycoforms. These include SL, SM, SH, and so forth. The glycoforms that we put into a SE profile could be produced by our candidate expression system. The parameters considered include all those mentioned in relevant regulatory guidelines such as ICH Q6B and the latest EMA guideline for MAbs.3 Figure 1 shows an example of an SE profile for glycosylation of a therapeutic MAb.

The SE profile is like a chessboard where the position of the pieces (the glycoforms) denotes their therapeutic power. The GCQAs relate to transformations of structural form and the power found when moving from one square to another. The greater the increase or decrease in power for a move, the greater the effect of that GCQA on the clinical performance of the drug.

An SE profile reveals two distinct classes of GCQA: Type I GCQAs connect glycoforms in different SE categories (i.e., information indicates that they alter a drug's clinical performance) and Type U GCQAs are glycosylation parameters that place glycoforms into the U and V categories (i.e. whether or not they alter a drug's clinical performance is uncertain).

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