Octet Potency Assay: Development, Qualification and Validation Strategies

1
APPLICATION NOTE
26
Octet Potency Assay: Development, Qualification and
Validation Strategies
Carson Cameron, Brendan Peacor, Nathan Oien, Andrew Cheeseman, and Jimmy Smedley, KBI Biopharma, Durham, NC
John Laughlin, and David O. Apiyo, ForteBio, Fremont, CA
INTRODUCTION
Kinetic analysis of biomolecular interactions is critical during drug
discovery and development. The affinity of an interaction directly
affects the dose required for a biopharmaceutical to be effective.
Real-time data on affinity and the kinetics of binding can provide
useful information at every stage of biopharmaceutical reagent
development. Moreover, understanding the mechanism of binding
can provide insights into the desirability of a drug candidate during
development, including implications for the drug’s stability upon
complex formation with its binding target. Binding kinetics assays
and specifically affinity constant (KD) analysis are increasingly being
used for biological products lot release. Regulatory requirements
necessitate that such products be QC tested using methods that
are robust and have been appropriately developed, qualified and
validated under GMP conditions.
In this application note, we discuss the strategies for the develop-
ment and validation of a potency assay using Octet systems. We
have highlighted the Octet system’s ease-of-use and fast time to
results by showcasing strategies for the development and valida-
tion of a method for evaluating the binding of an Fc gamma recep-
tor III molecule to the widely characterized NISTmAb.
Fc receptors are widely distributed cell-surface proteins that act as
communication points between effector antibodies and their bio-
logical implements. There are three classes of Fc receptors includ-
ing Fc-gamma receptor I (CD64) that is responsible for phagocyto-
sis and the activation of monocytic cell lines, Fc-gamma receptor II
(CD32) that is mainly responsible for antibody-dependent cellular
phagocytosis and Fc-gamma receptor III (CD16), which is respon-
sible for antibody-dependent cellular cytoxicity (ADCC). These
receptor molecules bind to antibodies through their Fc region and
Figure 1: Relative intensity of the light reflection pattern from the two surfaces on the biosensor. Octet BLI systems measure the
difference in reflected light’s wavelength (Δλ) between the two surfaces.
Incident
white
light
BLI signal
processing
∆ λ
Wavelength (nm)
0.2
0.4
0.6
0.8
1.0
RelativeIntensity
Biocompatible
surface
Bound
molecule
Unbound molecules
have no effect

OCTET POTENCY ASSAY Application Note 26
2
impart different activities. Glycosylation and other modifications to
the Fc region of an antibody can affect Fc gamma receptor binding
hence these receptor molecules are a good tool for evaluating
antibody drug efficacy and for antibody product lot release assess-
ment. In this application note, we use affinity constants (KD) as the
reportable parameter to determine Percent Relative Potency to a
reference lot.
BIO-LAYER INTERFEROMETRY
The Octet platform utilizes a Dip and Read format in combination
with Bio-Layer Interferometry (BLI) to monitor the interactions be-
tween biological molecules. BLI is an optical technique where white
light incident to a reflective biosensor surface immobilized with
a ligand results in changing interference patterns of the reflected
white light detected upon the interaction between the ligand and
the analyte which is kept in solution. Binding events between the
ligand and the analyte result in an increase in optical thickness on
the tip of the biosensor that can be measured as a wavelength shift
from the reference surface, and is a proportional measure of the
change in thickness of the biological layer (Figure 1).
MATERIALS AND REAGENTS
Materials and reagents for the studies:
Material/Reagent Vendor Catalog #
Ni-NTA biosensors ForteBio 18-5102
FcγRIIIa R&D Systems 4325-FC-050
NISTmAb National Institute
of Standards and
Technology (NIST)
RM8671
96-well plates Griener Bio-One 655209
384-well plates Griener Bio-One 781209
Octet HTX system ForteBio NA
Diluent (1X PBS,
0.1%Tween 20,
0.2% BSA)
KBI
(prepared day of use)
NA
METHOD DEVELOPMENT
Determination of analyte characteristics, including potency, can
be affected by the test method used. Factors such as the amount
of FcγRIIIa immobilized on the biosensor for the detection of the
analyte, the temperature and shaking speed of the reaction, the
sample matrix, and the equilibration time allowed for binding can
affect the binding behavior. Each of these factors can also impact
critical assay attributes such as accuracy. The Octet platform is
highly suited for a fast evaluation of the interactions between these
potential key assay inputs and allows for relatively high-throughput
method development. Three key input variables: temperature,
shake speed and ligand loading density, were identified to be
critical to the performance of the potency assay and were evalu-
ated in a mini-design of experiment (DOE) prior to establishing the
method. Each variable was examined at three or four levels. To es-
tablish assay performance at these conditions, the analyte concen-
tration, sample matrix and assay step run times were maintained at
constant values. A control condition was set at the ForteBio default
conditions for ligand binding assay (kinetics) with the shaking
speed at 1000 RPM and temperature at 30°C for these studies.
Optimal assay behavior can be split into four distinct parts for
kinetic determination assays:
1 The baseline(s) must be flat and absent of upward or downward
drift (Figure 2, 0–60 seconds).
2 The loading density should be adequate to ensure sufficient
protein is loaded on the biosensor, but not too high as to cause
steric hindrance of the subsequent binding event. Multiple
concentrations of the loading protein (Figure 2) are typically
evaluated, often with a following association step although this
is not mandatory. In general, low loading density is recom-
mended when using Ni-NTA biosensors.
3 The association step should show a concentration-dependent
signal over at least 1.5 orders of magnitude (in concentration)
as seen in Figure 2 (0 to 300 seconds). This step should also not
show binding heterogeneity unless it is known that there is a
2:1 binding event occurring as indicated by a sigmoidal curve
(similar to Figure 2) followed by a linear increase in signal rather
than a plateau.
4 The dissociation step should show at least a 5 % drop in binding
signal (Figure 4, 300 to 900 seconds).

3
Data Acquisition Setup
For most of the kinetics experiments, a 384-well plate was used
to enable high-throughput development (example shown in Fig-
ure 3). Additional buffer and loading wells were added as needed.
Control points were performed at the beginning of a run prior to
setting the shake speed or temperature to ensure no mechani-
cal effects (i.e. degradation due to increased temperature) on the
control sample.
Biosensor Selection
Ni-NTA biosensors were selected for this assay for robustness,
ease of use, and to take advantage of the commercial availability
of poly-histidine tagged (HIS-tag) receptors. FcγRIIIa is available
commercially with many different purification conjugates. Using
FcγRIIIa with a histidine tag at the C-terminus ensured the optimal
orientation of the protein binding to the biosensor and provided
the most distance between the biolayer of the biosensor and the
binding site to prevent hindrance of FcγRIIIa binding to NISTmAb.
Ligand Density Assessment
Proteins such as Fc receptors typically provide optimal loading at
concentrations <10 µg/mL. For this assessment, four concentrations
were analyzed in duplicate for optimal signal and lack of saturation
of the sensor (Figure 3). A FcγRIIIa concentration of 1.5 µg/mL was
initially chosen as the ideal loading concentration based on a signal
of 1 nm after 360 seconds, a typical initial benchmark.
Figure 3: Sample Diluent was used in buffer wells and the zero point of all curves. 8-point curves (including zero point) were plated in a single column.
Each baseline step had designated wells, and each curve used previously unused buffer wells for the dissociation step.
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2 3 4 5 6 7 8 9 10 11 12
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B – Buffer designated wells
L – FcγRIIIa designated wells
Sample or NISTmAb wells
White – empty wells
Figure 2: Four concentrations (10, 5, 2.5 and 1.25 µg/mL) were chosen for evalu-
ation while targeting 1 nm response over 300 seconds. The 1.25 µg/mL concen-
tration was chosen for further development. The baseline (first 60 seconds) and
the loading step (60 to 360 seconds) for all four concentrations are shown.
Time (sec)
Binding(nm)
0 50 100 150 200 250 300 350
0
0.5
1.0
1.5
2.0
2.5
A1
B1
C1
D1

4
Figure 4: A) Inverted signal of NISTmAb associating to a FcγRIIIa bound to the bisosensor tip (loading of FcγRIIIa not shown). B) Flipped data from A using the“Flip Data”
feature on Octet Data Analysis software. C) Typical wavelength shift (left to right) from a small change in optical thickness (ND = Optical Thickness). D) Wavelength shift
from a large change in optical thickness (right to left) resulting in an inverted signal.
Time (sec)
Binding(nm)
0 100 200 300 400 500 600 700 800
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0
Time (sec)
Binding(nm)
0 100 200 300 400 500 600 700 800
0
0.05
0.10
0.15
0.20
0.25
0.30
A B
DC
Step Step Type Time (s) Shaker Speed (RPM)
1 Baseline 60 1000
2 Loading 300 1000
3 Baseline 2 120 1000
4 Association 300 1000
5 Dissociation 600 1000
Table 1: Octet assay steps.
Antibody Binding
NISTmAb concentration scouting was performed beginning with a
range of 500 to 1.56 µg/mL. Curve shape, RMax, Chi2, R2, Global Fit
vs Local Fit, and Steady State were all considered when determining
the optimal antibody binding. These attributes were also evaluated
when establishing data processing parameters. The working range
was determined to be 200 to 3.125 µg/mL based on acceptable
assay performance. During the association step, signal inversion
occurred. Signal inversion is a less-known phenomena that arises
when the optical thickness at the tip of the biosensor experiences a
large change (Figure 4).1 This is usually attributed to large molecules
or complexes binding to a biosensor and is indicated as a decrease
in signal. To verify that binding is occurring and not dissociation, the
decrease in signal should be concentration-dependent and often a
following step should be included, such as a dissociation step. The
final assay steps are shown in Table 1 and were used for the theoreti-
cal pre-qualification/validation assessment.
0 5 10 15 20 25 30 35 40 45
-1.5
-1
-0.5
0
0.5
1
1.5
ND41
ND40
0 5 10 15 20 25 30 35 40 45
-1.5
-1
-0.5
0
0.5
1
1.5
ND51
ND40

5
Pre-Qualification/Validation Assessment
For NISTmAb binding to FcγRIIIa, a short screening assay was
performed as described above to determine the optimal FcγRIIIa
loading concentration and NISTmAb concentration range. A DOE
was then planned (Table 2) to determine the optimal loading con-
centration and if the platform conditions (30°C and 1000 RPM) were
suitable. A control preparation was performed at these platform
conditions (including 1.5 µg/mL loading) to observe day-to-day re-
peatability and to calculate %relative potency. The DOE approach,
coupled with the fast assay time of the Octet platform allows for
the method parameters to be scouted in minimal time.
Parameter Range Number of Points
Temperature 28–35°C 4
Loading Concentration 0.75–3.0 µg/mL 4
Shake Speed 800–1200 RPM 3
Table 2: Pre-qualification/validation development DOE.
The results were analyzed using statistical analysis software which
showed the optimal conditions for this assay were a 1.1 (±0.1) µg/
mL loading concentration for FcγRIIIa, a 1000 (±100) RPM plate
shake speed, and a 30°C (29.5-31.5°C) assay temperature.
Critical Process Parameter Assessment
The pre-qualification DOE also provided the data required to assess
specificity, precision/repeatability, and the working range of the as-
say. A diluent blank was performed as part of each NISTmAb curve.
These blanks all demonstrated no matrix interference, indicating
specificity of the assay. Due to the nature of the DOE, evaluating
precision required assessing the data points from center points of
the DOE. The average %relative potency was 91% with a %RSD of
7%, suggesting good precision of the assay. Further, all points in
the DOE showed R2 ≥0.97, suggesting the working range of the
assay (200 µg/mL to 3.125 µg/mL) is suitable for qualification.
The hydration of the biosensors was also evaluated. The baseline
signal immediately after biosensor hydration of 10, 15, and 20 min-
utes was comparable, demonstrating that a 10- minute hydration
time was suitable for the final method.
Method Qualification
Method Qualification, while not always required, can be a useful
tool in early phases of drug development and provide critical data
leading up to a validation. In general, the qualification of a potency
method involves evaluating linearity, specificity, accuracy, preci-
sion, and range. Method Qualification also serves to set system
suitability criteria for the assay as well as sample acceptance criteria
for release testing and/or stability samples. For instance, the results
from the accuracy calculations may allow for a criterion of 70% to
130% relative potency for test articles. When test samples meet this
criterion, they are considered equivalent to reference. The results
of an Octet %relative potency method qualification generally allow
criteria to be set for: R2, X2, maximum response signal, minimum
response signal, and a range of % Relative Potency (potency
comparison to reference). Typically, the results from running a
qualified method (during development or stability experiments,
etc.) in conjunction with the process (purification, culture, etc.) can
provide the data to set criteria for a validation protocol. Validation
of a method commonly repeats the studies, (linearity, accuracy,
etc.) performed in the qualification with tighter passing criteria and
includes significant ruggedness and robustness studies.
Method Validation
Method Validations are completed to ensure an analytical method
is suitable for its intended purpose. This provides an assurance
of reliability for routine testing in GMP environment. Validation
involves comprehensive protocol-driven experiments that evaluate
and document the performance of an assay. As this method was
being established as a potency assay, linearity, specificity, accuracy,
precision, range, robustness, and ruggedness were evaluated as
recommended by ICH Guideline Q2 (R1)3 “Validation of Analytical
Procedures: Text and Methodology.”
Linearity is the expected relationship between known potencies
of samples and their measured values using a range of 50% to
150% of the nominal relative potency, but treating them as 100%.
Five levels were tested over the 50% to 150% range including
100%. The R2 values of the resulting curves were all ≥0.95, indicat-
ing good linearity.
Accuracy is the degree of closeness to the expected value and was
determined using results obtained from the linearity studies by cal-

6
culating the percent recovery for each linearity level. For example;
a %Relative Potency of 46% at the 50% linearity level returns a 92%
recovery. The average %recovery was calculated to be 97% with a
range of 85% to 118% recovery. These results showed the method
was accurate.
Precision is the variability in the data from replicate determinations
under normal assay conditions. Repeatability of the method was
assessed by testing multiple preparations at the nominal load. The
average relative potency was 101% with a %RSD of 6%. Intermedi-
ate precision of the method was assessed using a second analyst to
test multiple preparations at the nominal load. The average relative
potency between two analysts was 101% with a %RSD of 8%. These
results were within the expected limit.
The range of the method is demonstrated when precision, ac-
curacy and linearity of the method show suitable performance.
Suitable performance was demonstrated spanning the working
range of 50% to 150% of the nominal potency. This corresponded
to 100 µg/mL to 300 µg/mL for the highest concentration of the
dose-response curve.
Specificity of the method was verified by testing a buffer blank and
a generic non-human antibody, both diluted in the same scheme
as NISTmAb. The %relative potency of the blank and generic
antibody were determined to be not-comparable to NISTmAb and
specificity of the method was confirmed.
Robustness of this assay was evaluated by testing the working
range of the parameters generated by the results of the develop-
ment DOE. This involved making small but deliberate changes to
the assay loading concentration, shake speed, and temperature.
These changes in methodology returned results within 70% to
130% proving the method is robust.
Ruggedness of this assay was tested by evaluating normal test
conditions that may vary over time. To test ruggedness of the assay,
a DOE was performed on the parameters with the most risk for vari-
ance. This included biosensor lot, FcγRIIIa lot, and analyst to analyst
variability (Tables 3 and 4).
The results of the DOE were analyzed by performing a Fit Least
Squares analysis. The results of this analysis are shown in Figure 5.
The Effect Summary Table showed no statistically significant
interactions (i.e. all ρ values were greater than 0.05). The Prediction
Profile and Interaction Profiler showed no clear substantial trends
between different variables. The effects of this DOE prove the
method is rugged.
Parameter # of Points
Biosensor Lots: 5
FcγR3A Lots: 2
Analysts: 3
Table 3: Ruggedness DOE.
Run Biosensor FcyR3A Analyst
KD
(nM)
%Relative
Potency
1 1 2 1 25 100%
2 1 2 3 21 93%
3 5 1 1 31 82%
4 1 2 2 30 109%
5 1 1 2 28 103%
6 2 1 1 23 112%
7 3 2 2 25 91%
8 4 2 3 24 80%
9 5 2 1 21 119%
10 3 1 1 22 114%
11 5 1 2 30 110%
12 3 2 3 22 88%
13 5 2 2 26 96%
14 3 2 1 20 127%
15 5 1 2 28 113%
16 1 1 2 22 86%
17 4 2 1 23 108%
18 3 1 2 27 105%
19 5 2 1 24 104%
20 1 1 1 19 109%
21 1 2 2 25 98%
22 1 1 3 18 105%
23 4 2 3 18 109%
24 3 1 2 30 115%
25 3 2 2 22 86%
26 1 2 1 23 109%
27 5 1 1 31 82%
28 5 2 2 25 97%
29 1 1 1 25 85%
30 3 2 1 33 76%
Table 4: A 30 run DOE showing the various combination of parameters tested.

7
Figure 5: A) Effect screen of multiple parameters showing no significant interactions. LogWorth = -Log (p-Value). B) Prediction
Profiler showing results are not able to predict future trends in data. C) Interaction Profiler showing the interactions between two
variables have no predictable effect on %Relative Potency.
A Effect Summary Table
C Interaction Profiler
B Prediction Profiler
Source LogWorth PValue
Biosensor*FcyRIIIa 0.677 0.21029
Biosensor*Analyst 0.629 0.23475
Biosensor 0.611 0.24497
Biosensor*FcyRIIIa*Analyst 0.551 0.28098
FcyRIIIa 0.216 0.60810
FcyRIIIa*Analyst 0.171 0.67448
Analyst 0.079 0.83416
70
1 2 1 23 1 2 34 5
80
90
100
110
120
130
97
[75.0558,118.944]
1
Biosensor
1
FcyRIIIa
1
Analyst
%RelativePotency
70
80
90
100
110
120
130
70
80
90
100
110
120
130
70
80
90
100
110
120
130
1
1
1
2
3
2
2 3 4 5 1 2 1 2 3
Biosensor
FcyRIIIa
Analyst
BiosensorFcyRIIIaAnalyst
1
2 3
1
2
11
22
3
4
5
4
3
5
%RelativePotency%RelativePotency%RelativePotency

8
41-0298-AN Rev A
Pall ForteBio
47661 Fremont Boulevard
Fremont, CA 94538
t: 888.OCTET-75
or 650.322.1360
Pall ForteBio Europe
5 Harbourgate Business Park
Southampton Road
Portsmouth, PO6 4BQ, UK
t: +44-(0)203 318 2840
www.fortebio.com
Pall ForteBio Analytics (Shanghai) Co., Ltd.
No. 88 Shang Ke Road
Zhangjiang Hi-tech Park
Shanghai, China 201210
t: +86 21 51915777
© 2018, Pall Corporation. Pall, , ForteBio,
Octet and Dip and Read are trademarks of
Pall Corporation. ® indicates a trademark
registered in the USA and ™ indicates a
common law trademark.
RESULTS
A %Relative Potency method for FcgammaR3 has been developed
and analyzed in a representative method validation. For this valida-
tion exercise, the representative raw data can be seen in Figure 6
and analyzed results in Table 5. The results show that this method is
linear, specific, accurate, precise, robust, and rugged over a specific
range in accordance with ICH Guidelines Q2 (R1).3
OCTET SYSTEMS IN GXP LABORATORIES
The use of Octet systems in GxP laboratories is constantly
expanding. KBI Biopharma has successfully developed 30+
methods on the Octet platform used for titer, potency, kinetics,
and identity testing. Many of these methods are being used to
support Manufacturing, Drug Substance or Drug Product Release
testing, and Long-term Stability testing in a GxP environment.
While the assay and sample acceptance criteria are dependent
on the method variability as well as the process variability, these
methods generally exhibit ≤10 %RSD between replicates over
long term testing.
Figure 6: Replicate binding curves (n=6) of NISTmAb binding to FcγRIIIa.
Time (sec)
Binding(nm)
0 100 200 300 400 500 600 700 800
0
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Parameter Reportable Result Result
Linearity R2 of triplicate preps R2 ≥ 0.95
Specificity Diluent and non-specific
mAb comparable to
reference
Not comparable
Accuracy %Recovery of linearity
preparations
85% to 118%
recovery
Repeatability Average %relative
potency and %RSD
Average = 101%,
%RSD = 6%
Intermediate
Precision
Average %relative
potency and %RSD of
Analyst I and Analyst II
Average = 101%,
%RSD = 8%
Range Method range 50% to 150%
for highest
concentration
Robustness %Relative potency at
modified conditions
70% to 130%
Relative Potency
Ruggedness Results of DOE No significance
from parameters or
interactions
Table 5: Results of the validation exercise.
CONCLUSION
Functional biological activity is a critical quality attribute (CQA) es-
sential to verifying the potency of a drug molecule.2 Potency assays
can be used throughout the development process in comparability
and formulation studies, and are required for release of every lot of
therapeutic protein. The Octet platform offers a fast, accurate, and
robust solution for measuring potency of a drug molecule. Here we
have described considerations for the development of a %relative
potency method capable of early-phase comparability studies and
subsequent method validation for lot release. With the speed of the
Octet HTX system, we could rapidly achieve Design of Experiment
results which led to development, optimization, and potential
validation practices.
REFERENCES
1 Application and Comparison of Algorithms for the Evaluation of Inter-
ferograms, G. Kraus and G. Gauglitz Fresenious’ J. Anal. Chem.,
349: 399–402, 1994.
2 ICH Guideline Q6B,“Specifications: Test Procedures and Acceptance
Criteria for Biotechnological/Biological Products.”
3 ICH Guideline Q2R1,“Validation of Analytical Procedures: Text and
Methodology.”

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