STEP 1: ESTABLISH THE GLYCOSYLATION COMPARABILITY SYSTEM
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).
Table 1. Candidate glycosylation critical quality attributes (GCQAs) for a typical monoclonal antibody
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
GCQA Tool #1: SE Profiles
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.
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).
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).