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16
PSR score
HIC retention time (min)
e
9 • Developability in Antibody Discovery 241
Process
14
12
10
8
0.00.2 0.40.6 0.81.0
FIGURE9.7 Bivariate plot of HIC retention time vs. PSR score for samples from 14 antibody discovery campaigns that led to a clinical candidate. The total number of samples
assayed was 2,848.
N= 48 (1.7%)
Candidat
9.7 ASSESSMENT OF CHEMICAL LIABILITIES
An additional important aspect of the developability of antibodies concerns their sta‑
bility in chemical degradation. Among the most common modes of modication are
asparagine deamidation, aspartate isomerization, and oxidation, primarily of methio‑
nine residues. As with some of the biophysical properties discussed earlier, there were
many early publications of an anecdotal nature, focusing on one or a small handful
of antibodies, with the main interest being primarily on method development. One of
the rst studies that included a relatively large sample of antibodies was published by
Sydow etal. in 2014.7 In this work, the authors examined potential deamidation and
isomerization events for 37mAbs that underwent stress treatment, described as incuba‑
tion for 2 weeks in a pH 6.0 buffer maintained at 40°C. They observed, for example,
that about 67% of sites with the NG motif showed proof of deamidation, while only
36% of DG motifs showed isomerization. Chances of deamidation were even lower for
motifs like NS, NN, and NT, as well as isomerization at DS, DT, DD, and DH, motifs.
The paper also described a machine learning approach using structural descriptors that
could be used to predict deamidation or isomerization from antibody sequences via
structural modeling.
A few years later, Lu, Nobrega, and coworkers at Adimab,9 using a similar set of
antibody samples as in the Jain etal. study, examined isomerization and deamidation
for 131mAbs that had been subjected to pH and temperature‑related stress. To acceler‑
ate potential deamidation, samples were incubated at pH 8.5 for 1 week at 40°C, while
for isomerization, incubation occurred at pH 5.5, 40°C for 2 weeks. This study included
an examination of 753 asparagine and 1,249 aspartate residues in the variable regions
of the 131 antibody samples. A summary of the data parsed out by motif class and by
antibody region is presented in Tables9.3 and 9.4, respectively. These tables also include
data (unpublished) from an additional 332 samples that comprise 1,364 asparagine and
3,029 aspartate residues in variable regions.
242 Biopharmaceutical Informatics
TABLE9.3 Summary of asparagine deamidation and aspartate isomerization data by
motif class
%
MOTIF CLASS TOTAL
MODIFIED
NG 30 47% 59 64%
NS 136 5% 376 6%
N [T, D, N, H] 129 10% 268 8%
NX 458 2% 1,414 2%
DG 46 35% 86 41%
DS 88 7% 308 6%
D [T, N, H] 319 2% 1,115 3%
DX 796 0.4% 2,769 0.5%
Source: Data from Lu, Nobrega etal.
a
Expanded set includes data from an additional 332 antibody samples (unpublished).
9
TABLE9.4 Summary of asparagine deamidation and aspartate isomerization data
by antibody region
ANTIBODY
REGION
TOTAL
ASN
%
DEAMIDATED TOTAL ASP
HCDR1 154 3% 92 3%
HCDR2 470 6% 320 4%
HCDR3 66 10% 783 6%
HFR 596 1% 1,008 0.3%
TOTAL,
EXPANDED SET
a
% MODIFIED,
EXPANDED SET
%
ISOMERIZED
LCDR1 443 16% 155 13%
LCDR2 190 3% 149 5%
LCDR3 129 3% 131 9%
LFR 69 0% 1,640 0%
Source: Data from 463 antibody samples, including those from Lu, Nobrega etal.
9
Table9.3 indicates that motifs are modied with percentages similar to what had been
reported by Sydow and coworkers7 on a smaller set, and this is in spite of differences in
stress conditions and general methodology. Modications at “unexpected” motifs, those
denoted as NX or DX in Table9.3, occur at low but observable rates. We break down those
by specic amino acid, including looking at the presence of glycine (G) in the position
prior to the N or D. It turns out that GN and NA motifs account for most of the cases with
detected deamidation at “unexpected” motifs. We observe that 12% and 15%, respectively,
of GN and NA sites have evidence of modication. In retrospect, the relative chemical
instability of NA should not be too surprising, given this motif having shown potential
for deamidation in prior work.
7,9
studies
were there examples of deamidation at NA detected, even though they were
55,56
It happens that in neither of the recent comprehensive
present in the sequences of some of the antibody samples assessed. All the instances of
modication at NA motifs occurred in the new set of samples summarized in Table9.3.
For aspartate isomerization, the motifs in the (initially) “unexpected” group are GD and
DE, with 2% and 3% of the cases, respectively, showing detectable modication.
9 • Developability in Antibody Discovery 243
Table9.4 summarizes the data where modications are located in the sequence for
the consolidated set of 463 antibody samples. Consistent with the literature consensus,
we see most of the modications occurring in CDRs with very few (none in the light
chain) observed in framework regions. There are marked differences among the dif‑
ferent CDRs, with CDR2 and CDR3 of the heavy chain and CDR1 of the light chain
exhibiting the highest relative rates of deamidation. A similar trend is observed for
aspartate isomerization, but here, CDR3 of the light chain also contributes to the tally
of modications, with a lesser contribution from CDR2 of the heavy chain.
These experimenta l studies make it clear that simplistic, purely sequence motif‑based
approaches to assess deamidation or isomerization potential are insufcient, and more
elaborate prediction methods offer improved alternatives.
55,57,58
While avoidance of
chemical liability hotspots, reliably predicted or experimentally conrmed, is consid‑
ered “best practice” in therapeutic antibody development, their relevance to activity
and/or safety is hard to assess in advance. An interesting recent example is that of the
FDA‑approved antibody crizanlizumab, which has been shown to undergo aspartate
isomerization at a DG motif within CDR1 of the light chain.59 This modication had a
deleterious impact on the potency of the antibody. However, it was also shown that the
change was reversible upon incubation in human serum. The authors of this report59
conclude that degradation leading to activity loss even under optimized formulation
conditions may not always be automatically excluded from development, given that bio‑
logical activity could be potentially restored under physiological conditions.
In addition to the oxidation of mainly methionine and tryptophane residues,
other modications in antibodies include glycation of lysine residues
mentation.
reviewed previously.
63,64
Discussion of other types of post‑translational modications have been
65,66
60–62
and frag‑
9.8 CONCLUSIONS AND FUTURE PERSPECTIVES
A robust investigation of multiple disparate sets of antibody molecules discussed above
supports the idea that a pair of relatively simple and high‑throughput assays, PSR bind‑
ing and HIC, are predictive of general developability behavior. Avoiding antibodies with
relatively high readouts in both assays seems a likely prerequisite for eventual success‑
ful development in the clinic. There are still several open questions. How unique is
the choice of PSR and HIC for this kind of proling? It is likely that combinations of
another assay that assesses “stickiness” or polyreactivity with an assay that measures
antibody hydrophobicity will yield similar results. Examples of the rst type of assay, in
addition to PSR, include CIC,4 binding to BVP,15 polyspecicity particle assay,67 single‑
and double‑stranded DNA, insulin and lipopolysaccharide ELISA,68 and protein panel
proling.
(SMAC) or Salt‑gradient Afnity‑Capture Self‑Interaction Nanoparticle Spectroscopy
(SGAC‑SINS) are potential alternatives in this context to HIC. Parting from the
69
Likewise, assays like Standup Monolayer Adsorption Chromatography
244 Biopharmaceutical Informatics
reasonable assumption that avoiding the high PSR, high HIC region, while perhaps nec‑
essary, is not sufcient to dene an antibody candidate as “developable,” at least two
more questions may be asked. First, will a more restrictive denition of “acceptable”
space in the bivariate plot using more conservative criteria, e.g., asking for both PSR
and HIC to be below the thresholds, or even lowering those thresholds further, lead to
an enhanced probability of success the development path? Second, are there additional
assays that may be routinely deployed in early discovery that could capture important
properties or features missing from the simple two‑variable analysis? Work in progress
in collaboration with other industry investigators aims at nding correlations between
high‑throughput assays that can be practically used at the earliest stages with more com‑
plex behaviors widely recognized to be critical for successful development. An example
would be viscosity, where a requirement for low values (an often‑used cutoff is 30 cP)
exists for high‑concentration formulations for subcutaneous administration, and prog‑
ress has been made on this front, both computationally and experimentally.
13,70 –73
Also
worth mentioning is the work of Bailly etal., which reports examples of correlations
between physicochemical properties and downstream process parameters.
74
Regarding the correlation of polyspecicity measures with pharmacokinet‑
14,15,17,20,21,75
ics,
presumably vesencumab,
it is interesting to look at potential outlier cases. The NRP1 antibody,
15,33
is an example where high readouts for the BVP, PSR, and
other polyspecicity assays correlate with observed high clearance in humans, but not in
cynomolgus monkeys (see Figure9.5 of the original work15). In a separate study, a set of
16 antibodies to infectious agents, seven against bacterial targets and nine against viral
targets, with collected terminal half‑life in humans20 is instructive as there is no putative
inuence from target‑mediated drug disposition. Samples were generated from the pub‑
lished variable region amino acid sequences, expressed transiently in HEK, and assayed
for several biophysical properties. The sample made with the sequences of the antibody
urtoxazumab is shown to have a high PSR score but the clinical molecule seems to have
expected pharmacokinetics with a reported half‑life of 26 days. When assayed for BVP
binding, however, the urtoxazumab sample shows a low score. Conversely, another anti‑
body with a normal terminal half‑life (24 days), anti‑HIV1 10–1074, gives a low PSR score
but a relatively high BVP score (see Figure9.8). A possible explanation for these seem‑
ingly contradictory results is that both assays and others in the polyspecicity class, work
by presenting a collection of multiple antigens. The composition and thus the behavior of
these collections will depend on the nature of the preparation and on the cell of origin (for
example, CHO for PSR, and Sf9 for BVP). It appears that the antigens in CHO‑derived
PSR that are responsible for the high response for urtoxazumab are likely to be absent in
relevant tissue in humans, and those antigens are also absent from the Sf9‑based BVP
mixture, so urtoxazumab does not appear as a false positive case in the BVP binding
assay. The converse situation may be occurring for the 10–1074 anti‑HIV1 antibody,
which appears as a potential false positive in the BVP but not in the PSR assay. Similarly,
for vesencumab, mentioned above, the antigens causing the high BVP binding score (and
a high PSR binding score; our unpublished observations with a sample generated from the
known variable region sequences) may be absent from cynomolgus monkey tissue, but
not from human tissue, so slow clearance is observed in the former, but not in the latter.
76
Progress in understanding important components in PSR particles has been reported,
and
9 • Developability in Antibody Discovery 245
1.0
Half-life (days)
PSR score
030
40
Half-life (days)
0.8
0.6
0.4
0.2
0.0
0102030
30
20
BVP score
10
0
0102
FIGURE 9.8 Correlation of terminal half-life from phase I human trials vs. PSR or BVP
scores for samples made from the respective antibody variable region sequences. Points
for urtoxazumab and 10–1074 are shown in red and purple, respectively. Data reported
originally in Shehata etal.
20
some of the results have led to alternative assays to probe polyspecicity.67 Uncovering the
underlying interaction in off‑target binding can be challenging so it is worth mentioning in
this context assays for identifying specic, but off‑target binding, such as cell microarray
technology.77 Though limited in throughput, they are gaining traction as means to prole
specic on‑ and off‑target binding in antibodies of clinical interest.
78
It should be noted that the studies reviewed here are concerned almost exclusively
with monospecic antibodies in the human IgG1 format. Some of the observations
may apply to other isotypes; recent work where isotype effects have been studied sys‑
tematically includes the publication by Tang etal.79 Similarly, while the relevance of
our observations to bispecic antibodies is not direct, it is the expectation that good
behavior by the component monospecic antibodies will be a necessary, but probably
not sufcient, condition for good behavior by the bispecic antibody, when built on an
IgG‑like format. Most of the assays discussed here for IgG molecules can be applied to,
especially, Fc‑containing bispecic antibodies. However, establishing clear metrics of
what ranges constitute concerning behavior, or a “ag” in the nomenclature described
earlier,12 is a work in progress. Some limited data generated internally has suggested
that properties like PSR, HIC, and others for bispecic antibodies often have readouts
in the range of the averages obtained for the individual monospecic antibodies. More
work is required to determine the generality of this preliminary observation. This is
an emerging area of research still providing surprises, as exemplied by recent work
where the exact arrangement of binding sites on two types of bispecic or bifunctional
molecules resulted in different pharmacokinetics.
80,81
In one of the examples,80 it was
observed that an IgG‑scFv fusion of antibodies 1 and 2 exhibited poor pharmacokinetics
in cynomolgus monkeys when antibody 1 was formatted as the Fab and antibody 2 as
the C‑terminal scFv fusion. However, when the orientation was reversed and antibody
1 was formatted as the C‑terminal scFv fusion, clearance was much slower and in line
with expectations of human IgG in cyno. Assessment in vitro of multiple biophysical
properties of the respective bispecic antibodies did not readily explain the differences.
These, of course, would be even harder, if not impossible, to explain from an assessment
of the individual monospecic components.
246 Biopharmaceutical Informatics
A nal area of the current investigation is the methodology for computational pre‑
diction from an amino acid sequence of PSR binding scores, or other metrics of poly‑
specicity or polyreactivity
82–86
and of HIC retention times.
87,88
Coupled with the kind
of analysis presented in this chapter and future correlations that may emerge, such pre‑
dictions are expected to streamline antibody discovery substantially. Even before such
predictions approach quantitative accuracy to the experimental values, we have found
them useful to recognize broad trends and in fact, have applied them to help design new
antibody repertoires (synthetic libraries) with which to initiate discovery efforts (Jain
etal., unpublished research).
ACKNOWLEDGMENTS
We are grateful to the Antibody Engineering, Protein Analytics, Platform Technologies,
Core Molecular Biology, and High‑Throughput Expression groups at Adimab for sam‑
ple and data generation. We thank Dr.Xiaojun Lu for access to his group’s unpub‑
lished chemical degradation data. We also thank Drs. Juergen Nett, James Geoghegan,
Arvind Sivasubramanian, and Robert Pejchal for reading the manuscript and for fruitful
discussions.
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