STEP 2: DEVELOP A TUNED GLYCOPROFILING SYSTEM TO MEASURE GCQAS
Figure 3. A measurement map for nonhuman Galα1,3Gal in a monoclonal antibody. Galα1,3Gal has been identified as a GCQA from
the drug safety–efficacy profile and impact maps. This measurement map shows the relative merits of various glycoprofiling
methods for Galα1,3Gal for this drug. The thickness of each measurement line is proportional to the suitability of the method.
The stars indicate the key methods chosen to measure Galα1,3Gal at different stages of the drug lifecycle.
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A flexible glycoprofiling system should be developed that is tuned to the drug's GCQAs (so they are measured reliably) and
tunable to different stages of your drug's lifecycle. This tuning can be aided by measurement maps. These are extensions of
impact maps and are obtained by listing all the glycoprofiling methods available and drawing measurement lines from each method
to each GCQA that it could measure. Figure 3 shows an example of a measurement map.
The glycoprofiling methods considered should include your inhouse glycoanalyses, methods commonly used in the industry, and
those outlined in the new USP Chapter <1084> on glycoprotein and glycan analysis and improved variants. In general, one needs analyses that separate and identify glycan
or glycoform species. This is typically achieved using a combination of high-performance liquid chromatography (HPLC) or capillary
electrophoresis (CE) and mass spectrometry (MS) plus sequencing grade exoglycosidases to analyze released oligosaccharides,
glycopeptides, and intact glycoproteins.1,4,5
The reliability of measurements is denoted on the measurement map by the thickness of the measurement lines, and the ease
of measurement for different stages of the drug lifecycle can be denoted by coloring or annotating the lines. Measurement
reliability should be assessed according ICH Q2(R1) or similar guidelines, for analyses performed on a particular drug. Measurement
maps to aid the selection of a set of glycoprofiling methods that provide either relative or absolute quantitation of the
full range of GCQAs.
It is usually not feasible to measure all GCQAs at every step in a product's lifecycle. However, they should all be measured
before and after product scale-up. Also, for effective comparability studies, companies should aim to identify all the major
peaks in HPLC, CE, and MS glycoprofiles early on in drug development.
STEP 3: DESIGN A QTPP WITH OPTIMIZED GLYCOSYLATION
The next step in the QbD framework is to define the drug's quality target product profile (QTPP). For novel biopharmaceuticals,
this should include optimized glycosylation to maximize the drug's clinical performance.
In terms of SE profiles, a completely optimized drug will have only SH (safe, high activity) and SM (safe, medium activity)
glycoforms. Compared to the unoptimized therapeutic, such a drug would tend to be:
- less sensitive to changes in its clinical performance following glycosylation changes typically found during scale-up
- more potent, so requiring a smaller level of scale-up
- less heterogeneous, being composed of fewer glycoforms
- lower in cost per therapeutic dose.
To illustrate the importance of glycan optimization, consider a drug composed of two types of glycoforms, H and L (e.g., afucosyl
and fucosylated MAb with ADCC as mode of action or highly sialylated and low sialylated gonadotropin), where the H form has
10-fold greater activity than the L. In an unoptimized drug with 88% of L form, 12% of H forms would contribute over 57% of
the total drug activity. If the specification for the H forms was 12% ± 3% the drug activity could vary from 1.81 to 2.35
(~30% variation in total activity from low to high limits). In an optimized drug with 88% of H forms, those would contribute
over 98% of the total drug activity. If the specification for the H forms was 88% ± 3%, the total drug activity could vary
from 8.65 to 9.19 (~6% variation in total activity from low to high limits).
Glycan optimization starts with picking an expression system with the appropriate glycosylation machinery.6 For example, MAbs expressed in NS0 cells can contain glycosylation with potentially immunogenic nonhuman epitopes (Gala1,3Gal
and NeuGc) as well as high levels of core fucosylation (so the dominant glycoforms fall into the undesirable N and L categories).
Switching to Chinese hamster ovary (CHO) cells reduces the N and L category glycoforms at a stroke, thus resulting in a safer,
more potent product. Further improvements could be made by using one of the growing number of alternative expression systems
with improved or tunable glycosylation.
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