Beyond Measuring Affinity:
Biacore to take Center Stage in Orthogonal Analytics for Comparability Exercises and Quality
Tasks of Biologics.
Biacore QC Workshop
Christian Maasch, Xendo
Managing Consultant/Team Lead Drug Development and RA Biologics
Bringing scientists together...
Vienna, Nov 23nd , 2017
Confucius (Chinese philosopher) said …
2
The route/way becomes the destination
… this is also true for the development of using Biacore …
Failure Is Not an Option -- It's a Requirement
2013 first Biacore paper about PK and Biomarker analysis published
1998
Retrospective Look of “Working w Biacore”
3
Christian´s personal time line
2003 first Biacore paper published
2009 Director of analytical department
2010 presented using Biacore as QC/ potency assay
2011 presented using biophysical assayas stand-alone batch release assay
2011 DiPIA Boston presented concept of bioanalytics by Biacore
• Finally we are here to meet and talk about regulatory compliant QC/ potency assay and applications beyond
• Thank you for the chance to be part of this Biacore-family for more than nearly two decades and to spread these (once) “weird ideas” for using Biacore“beyond measuring affinity”.
2000 purchased first “own” Biacore
1998 started working w Biacore at MPI Berlin
2017
4
Lessons learns by using Biacore for
more than 15 years …
5
The Protagonist and What It Measures: Biacore
Measures:
Binding rates constants ka and kd of an analyteto a sensor chip immobilized target
Data output:
Binding association rate constant ka
Binding dissociation rate constant kd
Binding affinity (KD)
Stoichiometry or number of binding sites (n)
Enthalpy ΔH
Entropy ΔS
Concentration (c) of unknown analyte
Biacore/ SPR
-60
-40
-20
0
20
40
60
80
100
-100 0 100 200 300 400 500 600
Tim e s
Re
sp
. D
iff.
RU
Spiegelmer
binding to target
ka [1/M*s] 2.72E+06 ± 2.10E+04
kd [1/s] 2.86E-03 ± 1.35E-05
KD [M] 1.05e-9
kakd
BC 3000
BC T200
Biacore sensorgram
Information-rich data, but…
Know and understand your assay; do the right controls !
from Drug Discovery to Clinical Development
Ph.II: Patients
Drug
Discovery
Preclinical
Discovery
Preclinical
Development Manufacturing
and QC
Ph.I: Healthy
volunteers
Clinical
Development
Use of Biacore Methods in Drug Discovery at
NOXXON
7
D40D32
D30
D09
D17D16
association rate log ka (M-1
s-1
)
dis
so
cia
tio
n r
ate
lo
g k
d (
s-1
)
5.8 5.9 6.0 6.1 6.2 6.3
-3.2
-3.1
-3.0
-2.9
-2.8
-2.7
-2.6
-2.5
faster association
slo
wer
dis
socia
tion
NOX-D19001-6xDNA
kc
al/m
ole
-60
-40
-20
0
20
40
G
H
-TS
Hit identification/ binding kinetics and affinity
Lead optimizationor screening
Thermodynamics/ transition-state
Selectivity/ specificity Mode-of-Action (MoA) and structure-function relationship
Biacore substantially supported the discovery of the “Spiegelmer” therapeutics
Proven Value of the Biacore Technology for
Drug Discovery
8…. but we think there is much more potential….
Accepted technology (see below recent publication of 2 articles back-to-back on April 22, 2015)
Positive Experience with Biacore …
9
Well developed biophysical/ Biacore assays …
Can we transfer our positive experiences to preclinical and clinical R&D ?
…are robust and reproducible
...have a high precision and accuracy
22 repeated analysis
time [s]
resp
on
se u
nit
s [
RU
]
0 200 400 600
0
100
200
300% theoretical content of active Spiegelmer
Biacore
100% 99.69±0.21
98.04% 98.18±0.15
96.15% 96.82±0.26
94.34% 93.88±0.12
92.60% 92.26±0.20
90.90% 90.82±0.18
80.00% 79.28±0.21
10
…, but what I also learned …
Paul Belcher on Linkedin/Twitter:
“… I see Senorgrams everywhere”
11
Paul Belcher Functional Leader BiacoreGE Healthcare, US
Paul Belcher on Linkedin/Twitter
12
“… following on from my blog posts about data fitting andwhen to believe the data or not - I saw this data in apublication this week. Discussion...3...2...1 and GO”
Paul Belcher on Linkedin/Twitter
13
“… following on from my blog posts about data fitting andwhen to believe the data or not - I saw this data in apublication this week. Discussion...3...2...1 and GO”
“… we might have to define a new unit.....10e-24 is Yoctomolar” Paul B.
“It is always worth to use bothkinetic and steady state fittingsas a self-QC. And 900 binging RUwithout saturation.... scary. “Shuo W.
“I suspect those aren't rate constants,but rather the author is referring toequilibrium constants KA and KD. Thatwould agree with the numbers anyway.Still does not appear to be 0.5 pMinteraction.” Michael M.
“Association constant is10e12 M... what a fastbinding! Is it super glue?”Secil F.
“I'm going to guess at some of the other numbersgenerated by the fit: Rmax <2 RU. RI up to 1000RU. Even if this was a steady-state fit, they stillhaven't approached saturation closely enough togenerate plausible KD values.” Eric R.
“The numbers given are obviously not the rateconstants ka and kd but the equilibriumassociation and dissociation constants, asKa=1/Kd. Still the dimensionality of Ka should beM^-1, not M. Otherwise with on and off ratesbeing too fast to be measured telling trueresponse from bulk RI change is tricky if, as in thiscase, the system is far from saturation. Showingthe response vs. concentration plot is the leastthe referee should have asked for.” Andrei Z.
BBP – “Best Biacore Practice”
14
Know and understand your assay; do the right controls
Describe your assay setup and evaluation/kinetic model with respect to the MoA of your analyte (show the fit!)
Critical evaluate your data; potentially look for a “Biacoresparring partner” to discuss results
Learn “reading your sensorgram data” without software
Reading, “Dissection” and Interpretation of
Senorgram Data
15
… start “reading sensorgrams” instead of “blind trusting” the software …
Sensorgram taken from: Humanization and Characterization of an Anti-Ricin Neutralization Monoclonal Antibody, Sep 2012 · PLoS ONE
KD = 1.63 nM
Baseline: • Slope? Drift?• Memory effect?• Chip integrity?• Delay in injection? IFC or flow to slow?
Association Phase :• Saturation?• Rmax used for fitting;
calculation of ka?• Curvature sufficient?• Biphasic association
(fast/slow) ->Non-specific binding?
• Low signal for mAb@500 nM, must be saturated at lower concentrations when KD=1.63 nM
• Surface activity and control?• Initial on-rate: is it concave?
MTL?
Regeneration:• Efficient?• Monitor chip decay! Or memory effect
Evaluation:• What model?, fit and goodness of
fit not shown!• Why KD ~1.63 nM, but there is no
saturation at higher concentrations
• No standard dev (but, 3 digits for rate constants ;))
Dissociation Phase• Off-rates of differ!• NSB or bivalent or heterogeneous
ligand?
Assay setup: • Concentration range
tested feasible for estimated affinity?
16
Towards Validation of Biacore Assays
Lessons Learned by ELISA Assays, Cell-based Potency
Assays and HPLC Analysis
• Standard Curve/ “Internal” Reference Material• Calibrators/QC sample analysed in duplicates/
triplicates to monitor assay performance• LOD/ LLOQ/ ULOQ• Evaluation of sample of unknown
concentration: standard curve was calculatedby fitting a 4-parameter logistic function (4-PL-function) to the raw data of the calibrator andwas used to calculate the concentration of theunknown samples thereof.
• Evaluation of relative “binding potency” of known sample by dose-response curve: e.g. by parallel-line analysis/ IC50
ELISA and cell-based assays are steady-state analysis: How to transfer to a Biacore analysis?
Biacore Evaluation:
Steady-State vs. Kinetics – do they fit?
18
KD steady state : 0.87µMKD kinetics: 0.91µM
• In an ideal world and under optimalassay conditions steady stateanalysis and kinetics fit well
-50
50
150
250
350
450
-200 0 200 400 600 800 1000
Tim e s
Re
sp.
Dif
f.
RU
Awful Fit, Chi2 = 740KD ~ 46.6 nM (kinetics) vs 481 nM (steady-state)
Steady state?
• In a real world parameters like e.g.non-specific binding, heterogeneousbinding, assay limitations, bad datafitting, … may influence that steady-state analysis does NOT fit to kineticdata
From Report Points to Standard Curve
19
time [s]
res
po
ns
e u
nit
s [
RU
]
0 200 400 600-200
0
200
400
600
Report points• Standardized and “easy-to-evaluate/transfer”
data to analysis software, e.g. Prism, PLA 3.0 or SoftMax
• Fit data to a non-linear 4/5-parameter fit
Assay Set up and Development : Report Points and Standard Curve
concentration analyte [nM]
res
po
ns
e u
nit
s [
RU
]
0.01 0.1 1 10 100 1000
0
500
1000
1500
2000
2500
Hill slope = 1.12
IC50 =318.1
Bottom
Top
DETERMINE STANDARD CURVE• Define fitting parameters and confidence
intervals for assays to “pass”
Introducing QC Samples/Calibrators to Your
Biacore assay
20
Conc. ± SD % accuracy %CV
QC 0.5nM 0.582±0.0212 105.5±4.15 3.64
QC 2.0nM 2.221±0.1329 111.1±6.6 5.98
QC 10.0nM 12.40±1.066 124.0±10.66 8.60
QC 50.0nM 55.66±3.031 110.8±7.6 5.44
Accuracy 100% Conc
MeanAcc results
Precision 100% results
results
Mean
SDCV
1
• Role of QC samples is that they represent the matrix of the samples with known amounts of the analyte; QC samples are processed in the same manner as study samples
• Used during method validation to demonstrate e.g. accuracy, precision and stability
• Subsequently used during the conduct of the study to provide batch-level quality control
Biacore Assay Acceptance Criteria:1) A minimum of 75 % of all calibration values in the working range (between the LLOQ and ULOQ) have to show an accuracy
of 80-120 %, except at the LLOQ and ULOQ where limits of 75-125 % are acceptable.2) At least 4 out of 6 QC samples at three concentrations have to show an accuracy of 80-120 % and a precision of ≤20 %. At
least one QC sample per concentration needs to meet this criterion. If this criterion is not fulfilled, the assay is invalid andthe samples must be re-analysed.
Example for Competitive Biacore Assay:
Monitor Assay Performance by Bracketing
21
injection cycle
resp
on
se u
nit
s [
RU
]
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
0
100
200
300
400standard_start unknown samples and QC samples
no comp
200 nM
standard_end
no competition control
Sequential analysis of samples
initial 3 injections
res
po
ns
e u
nit
s [
RU
]
0
1
2
3
4
1
23
2 x standard curve
concentration analyte [nM]
res
po
ns
e u
nit
s [
RU
]
0.1 1 10 100 1000 100000
100
200
300
400standard curve start
standard curve end
Additional Information by Real-time Measurement:
Kinetics to bring Value and Confidence to your Assay
22
time [s]
res
po
ns
e u
nit
s [
RU
]
0 200 400 600-200
0
200
400
600 kakd
Qualitative and quantitative data in a sensorgram• ka [1/ M*s]: a function of the concentration or/and activity/ integrity of the analyte• kd [1/s]: reflects the binding mode of action (MoA)/identity of the analyte
KNOW YOUR ANALYTE AND ASSAY !• Measure ka and kd and define acceptance criteria
for your analyte
ka
ka [
1/M
*s]
1.0106
1.2106
1.4106
1.6106
1.8106
2.0106
kd
kd [
1/s
]
1.010- 4
1.210- 4
1.410- 4
1.610- 4
1.810- 4
2.010- 4
EXAMPLE:
Biacore as Surrogate Potency Assay
23
12.5nM – 6.25 – 3.13 – 1.56 – 0.78 – 0.39 – 0 nM
-10
0
10
20
30
40
50
60
-100 0 100 200 300 400 500 600 700 800
respo
nse u
nits [R
U]
time [s]
double injection of 12.5nM
Read-out report pointsP1, P2 – slope, baseline stabilityP3 – RU after injection time vs concentration (Req?!)P4, P5 – regeneration efficiency
Biacore fingerprint: -> sensorgram comparison tool;Sum of evidence for quality of data
P1 P2 P3
Read-out binding kineticska – association rate constantkd (1) – early dissociation rate constant kd (2) – late dissociation rate constant P4
regeneration
P5
ka kd (1) kd (2) Read-out data fittingApply global fitting model !!!RI – bulk or unspecific binding contributionChi2 – parameter for “goodness of fit”, ruol ≤ 1(-5)% of Rmax
Adjust kt value if appropriate
Intra assay controls/assay performance• 6 QC samples (4 in linear range)• accuracy of 80-120 % and a precision of ≤20 %.
Biacore Assay KPIsEstablish ranges (e.g. CI) - pass or fail?
24
KPIs = Key Performance Indicators (kinetic analysis)
• Know AND understand how your assay setup/parameters (and their correlation) affect the performance of the assay (DoE/QbD approaches)
• Establish reliable ranges and statistics for the definition of acceptance criteria
QC1 QC2 QC3 QC4 QC5 QC6 QC1 QC2 QC3 QC4 QC5 QC6
* Relative “binding potency”: KDrel = mean KD samples/ KD internal standard
Further Biacore KPIs
25
• Acceptance criteria for baseline, blanks/matrices, non-functional analytecontrols…
• Acceptance criteria regeneration efficiency• Is curvature for appreciate number of concentrations established?• Rmax consistency vs injected concentrations -> monitor NBS contribution• Double injections -> chip integrity• monitored Rmax, Req (local) -> validity of kinetic data• …
Giordana Bruni is going to talk about that topic and share her experience
Example: Clinical IMP Batch Release
26
Cell-based Assay
Relative potency = 76.25 %
Comparable results between biophysical and cell-based assay format
Spiegelmer (nM)
flu
ore
scen
ce
(rf
u)
0.01 0.1 1 100
10000
20000
30000
40000
50000
reference IC50 = 0.4159 nM
IMP_7 batch IC50 = 0.5454 nM
inter- and intra-assay variations
% a
cti
vit
y o
f re
fere
nc
en=1 n=2
0
50
100
150
% IMP activity ofreference batch
% a
cti
vit
y o
f re
fere
nc
e
IMP_7 batch
0
50
100
150
Biophysical: Biacore
p= 0.3415, ns
Relative binding „potency“ = 78.49 % ± 4.867 (SD)CI95% of mean = 75.40 – 81.58%
Why a Biophysical Assay ?
27
Technically less challenging
No batch-to-batch variation as in animal bioassays
No limitations of handling cells in cell-based bioassays
Higher precision and sensitivity
Not subject to inherent variability of bioassays (e.g. passage number)
Assay-to-assay consistency of critical reagents
Reagents may be frozen and used over a long period of time
Combines robustness with high sensitivity, precision, accuracy and low sample consumption
Regulatory Perspective:
Biacore Beyond Lead Generation?
28
new guidance document for analytical method validation (02/2014)
What´s essentially new?
Section III on 'Analytical Methods Development' Emphasis on getting the method right at the beginning with respect to specificity, linearity, limits of detection (LOD) and quantitation limits (LOQ), range, accuracy, and precision and method robustness
Encourages to find (new) analytical methods with best analytical performance characteristics
Development Research IND Enabling Clinical Post-market
IND e.g. BLAIND = investigational new drug; BLA = biologics license application
Regulatory requirements
RA Compliance:
Biophysical Assay as Surrogate Potency Assay?
29
According to ICH Guideline Q6B, a biological assay to measure thebiological activity of the product may be replaced by biophysicaltests only in those instances where:
sufficient mode-of-action (MoA) information about the drug…
and relevant correlation to biologic activity can be demonstrated…
and there exists a well-established manufacturing history…
Animal-based Cell culture-based Biophysical ???
Accepted as potency assay
Correlation of Binding Affinity to Biologic Activity?
30Kd (nM;biophysical)
IC5
0 (
nM
; c
ell
-ba
se
d a
ss
ay
)
0.1 1 10
0.1
1
10slope = 0.9052 0.1675
NOX-A12
NOX-D20
NOX-L41 NOX-E36
NOX-H94 NOX-G16
NOX-S93
STHKRTG a )ln(
R
S
TR
HKa
1)ln(
Short reminder / tutorial – binding and thermodynamics belong togethervan’t Hoff analysis:
Excellent correlation of binding affinity and biological activity/
potency for oligonucleotide-based scaffolds
Temperature matters ! – measure at 37°C !
Long-term Experince of Using a Biophysical
(Surrogate) Potency Assay
31 Competitive binding assay, low intra- and inter-assay variations, higher precision and accuracy than cell-based assay and linear correlation with biological potency assay
… used since 2010 as stand-alone surrogate potency assay for batch release
1 10 100 10002.0
2.5
3.0
3.5NOX-H94
80/20
50/50
IC50
NOX-H94
22.57
80/20
~ 33.21
50/50
~ 2.768e-012
SPM (nM)+20nM HEP
Ferr
itin
(n
g/µ
g P
rote
in)
1 10 100 10002
3
4
5
6NOX-H94
80/20
50/50
IC50
NOX-H94
13.12
80/20
13.78
50/50
29.98
SPM (nM)+20nM HEP
Ferr
itin
(n
g/µ
g P
rote
in)
1 10 100 10002.0
2.5
3.0
3.5
4.0NOX-H94
80/20
50/50
IC50
NOX-H94
26.27
80/20
23.55
50/50
56.52
SPM (nM)+20nM HEP
Ferr
itin
(n
g/µ
g P
rote
in)
week 1 week 2 week 3
Cel
l-b
ased
ass
ay
0.1 1 10 100 1000 100000
20
40
60
80
100
120
nM SPM
Bin
din
g o
f D
IG-l
ab
ele
d N
OX
-H9
4
to
bio
-HE
P(%
)
0.1 1 10 100 1000 100000
20
40
60
80
100
120
nM SPM
Bin
din
g o
f D
IG-l
ab
ele
d N
OX
-H9
4
to
bio
-HE
P(%
)
0.1 1 10 100 1000 100000
20
40
60
80
100
120
nM SPM
Bin
din
g o
f D
IG-l
ab
ele
d N
OX
-H9
4
to
bio
-HE
P(%
)
week 1 week 2 week 3
50/50
80/20
NOX-H94
50/50
80/20
NOX-H94
50/50
80/20
NOX-H94
Bio
ph
ysic
al a
ssay
32
Further tasks using Biacore in CMC
Biacore Assays used to Support CMC for …
QC/ batch release
IPC = In process control/ QbD support (e.g. AMBR studies= rapidly screen and optimize cell culture processparameters) -> identification of CQAs/ comparability studyfrom process performance to product quality attributes
Formulation development/Stability tests (e.g. total vs active DS)
Comparability studies
AP
I/D
S
Surrogate potency assay (clinical batch release)
Formulation development/Stability tests
QC/ re-analysis/ in-process controls
IMP/
DP
New process Establish process and analytics Establish ranges/ QbD Identify CQAs
Biosimilars … coming back to Confucius
34
The route/way becomes the destination… this is especially true for the development of Biosimilars
Biosimilar
QTPP Biosimilar-> GOAL: Innovator QTPP in mind !!!
Biological/ Innovator drug
QTPP = Quality Target Product Profile
Innovator drug challenge:QTPP not known/available
Are we opening Pandora´s Box by applying new processes and technologies?
The CMC Challenge of a Biological
35
The very nature of a biologic means It is practically impossible for two different manufacturers to produce two
identical biopharmaceuticals if identical host expression systems, processesand equivalent technologies are not used
This has to be demonstrated in an extensive comparability programfor biosimilars
Biosimilars do have a few Developments in their Favor
Technological advances to characterize biologic molecules and theanalytics used to evaluate and prove biosimilarity.
Improvements are being made in the manufacturing techniques used toproduce biosimilars.
Structure Function Relationship – Orthogonal
Approaches
36
• Understanding the structure-function relationship of a biotech product isvital for its successful development.
• Awareness of this is important, and by selecting the right assays at theright time, it is possible to reduce significantly the costs and risks of theR&D process.
• By applying orthogonal methods and approaches for characterization,information regarding the structural, binding interactions and functionalproperties of the biological product are linked – providing a greaterunderstanding and interpretation of the determined characteristics.
PotencyPurity
Structure
Effector function MoA In vivo, eg. PK/PD/Immunogenicity
Additional Tasks for Biacore in Analytics
37
Affinity andBinding kinetics
Concentration/Purity(total vs active)
Surrogate Potency Assay
IPCs/ process development/CQA
Identity/Integrity
Stability
Structure-Functionrelationship
Receptor binding assays
Formulation
(Accelerated) stability studies
Off-Target binding
Immunogenicity
Example: Structure-Function
Relationship
38
Orthogonal Use of Binding Analysis (SPR) for Structure-Function Relationship
Target binding: Affinity, rate constants and selectivity, specificity by SPRAdvantage: High accuracy, precision and sensitivity, low inter- and intra
assay variations
Purity: HPLC/ SEC data linked to total and active DS/DPAdvantage: Linking the information of total to active DS/DP and refer to
potency
Identity/integrity: LC-MS, Disulphide bridge, DSC analysis linked to SPR rate constants as “fingerprint” and prediction of MoA
Advantage: Confidence in structure-function relationship
Glycosylation: Abundance of a glycosylation species was linked to effector receptor binding as determined by SPR
Advantage: Bridging glycosylation pattern and effector function by descriptive (binding) MoA
39
Potency: Cell-based assay linked to SPR assayAdvantage: Only One Cell-Based Assay acceptable to demonstrate
bioactivity
Effector function: Binding of FcgRs linked to cellular cytotoxity (ADCC)Advantage: Linking effector function as determined by cell-based assays to
descriptive (binding) MoA
FcRn Binding: Used as total sum of evidence for identify/ integrity and linked to FcRn binding assays as prediction of pharmacokinetic properties
Stability: see SPR analytics above
Example: Structure-Function
Relationship
Stuart Knowling (BioOutsource) and Mike Willcox (Eurofins) have already shared their experience and insights
Additional Information from Sensorgrams linked to
other Analytics
40
CONFIDENCE IN DATA AND ”PIVOTAL” EVIDENCE OF ANALYTICAL SIMILARITYby best practise analytics and orthogonal approaches
AGENCIES are happy
REDUCTION OF ANALYTICAL EFFORTSdriving the reduction of costs and risks
SPONSORs are happy
Where Do We Go? –
The Challenge of Crude Sample Analysis
41
PharmacokineticsWhat is the half-life t1/2 of a drug in the blood ?
Pharmacodynamics/ BiomarkersHow long is the drug effective ?
Bio-distribution/ADMEWhere is the drug distributed in the body and where is it metabolized or cleared ?
ImmunogenicityDoes the drug induced anti-drug antibodies (ADAs) ?
The Problem of Plasma Binding and Cut-
Point Analysis
42
Binding does not equal functional/ biological significance
Unspecific binding of biological matrices reduces the assay’s detection limit
Unspecific binding to various surfaces differs
From: Biacore Immunogenicity Package Manual
-50
0
50
100
150
200
-50 0 50 100 150 200 250 300 350 400
Tim e s
Re
sp
. D
iff.
RU
Report point 1
Report point 2
Rat plasma binding todextran Biacore surface
res
po
ns
e u
nit
s [
RU
]
report point 1 report point 20
50
100
150
200
0%
0.0039%
0.0078%
0.0156%
0.0313%
0.0625%
0.125%
0.25%
0.5%
1%
The Real Challenge: Individual Cut-points!
43
Unspecific binding of sera from healthy volunteers to an immobilized chemokine
Reducing unspecific binding is mandatory for further assay development
res
po
ns
e u
nit
s [
RU
]
healthy volunteers0
1000
2000
3000
4000
5000
Unspecific binding of 250 individual sera from healthy volunteers differs dramatically
Definition of a cut-point is challenging in terms of the assay sensitivity (more likely it is impossible)
The only way out: pre-dose vs treated normalization of data
What if there is no pre-dose sample available?
• Unspecific binding wasreduced to a negligiblelevel at NOXXON Pharmaby optimized bufferconditions and the use ofunspecific competitors(e.g. soluble dextran)
• Methods established forthe analysis of plasma,serum, urine and issuehomogenates
Example: Compound (Scaffold)
Pharmocokinetic/ Pharmacodynamic In Rats
44
0´ 10´ 1h 3h 8h 24h 48h 96h
Blood sampling
Organ Dissection
Biacore analysisPlasma pharmacokineticTissue distributionTarget/ Biomarker plasma levels
Compound (Spiegelmer): L-nucleic acid linked to PEG
Biomarker
Overview of Assay Performances for
Bioanalytics by Biacore
45
Assay L-Nucleic Acid Qualification PEG Qualification Biomarker Quantification
Assay type Direct binding Biacore assay Competitive Biacore Assay Competitive Biacore Assay
Matrices Plasma, liver, urine Plasma, liver, urine Plasma
Sample preparation None; simple dilution None; simple dilution
On-target effects No influence; up to 1µM tested No influence; up to 1µM tested No influence; up to 1µM tested
Biological matrices No influence; (up to 50%) No influence; (up to 30%) No influence; (up to 50%)
In-use stability of chip > 500 analysis cycles > 350 analysis cycles > 310 analysis cycles
LLOQ ≤ 0.2 nM ≤ 1.95 nM 0.2 nM
Precision %CV < 3 %CV < 8 % CV < 7
% Accuracy 98 -103% 93 -110% 97 -104%
Dilution linearity % Recovery
Yes> 98%
Yes > 96%
Yes>93%
Time per sample 9 min 7 min 7 min
concentration Spiegelmer [nM]
res
po
ns
e u
nit
s [
RU
]
1 10 100 1000 10000 100000
0
500
1000
1500
2000
2500
Hill slope = 1.12
Spiegelmer
Spiegelmer + 1µM target
Spiegelmer concentration [nM]
% b
ind
ing
of c
on
tro
l
0.1 1 10 100 1000 100000
20
40
60
80
100
IC50 = 121.1 nM
Hill slope = -0.78
4-PL fitted standard curve
concentration of biomarker/ target [nM]
re
sp
on
se
un
its
[R
U]
0.01 0.1 1 10 100 10000
50
100
150
200
250 target
target + 1µM Spiegelmer
no comp
Hill slope = -1.040/ -1.025
Questions Answered by Biacore
Bio-Analytics
46
sampling time [h]
Sp
ieg
elm
er
pla
sm
a c
on
c. [
nM
]
0 24 48 72 96
100
1000
10000
100000
quantified by oligonucleotide
quantified by PEG moiety
Plasma pharmacokinetics
How long is the compoundin the blood?
Target/ Biomarker pharmacodynamics
What is the efficacy of the compound?
sampling time [h]
Bio
ma
rke
r in
pla
sm
a [
nM
]
SD
0 24 48 72 96
0
200
400
600
800
1000
1200
1400
release rate = 40.81 2.916 nM/ hour
Cmax = 890.1 273.8 nM at 48 hours
Tissue distribution
How is the compound cleared from the body?
Renal clearance
% o
f to
tal d
os
e
SD
24 h 96h0
2
4
6
liver
% o
f to
tal d
os
e
SD
24 h 96h0
2
4
6
kidney
% o
f to
tal d
os
e
SD
0-24 h 24-48h0
1
2
3
4
Bound-free Analysis
When do I have to dose again for the best effect?
sampling time [h]
pla
sm
a c
once
ntr
atio
n[n
M]
0 24 48 72 96
0.1
1
10
100
1000
10000
100000
target
Spiegelmer
sampling time [h]
% a
cti
ve
of
co
ntr
ol
0 24 48 72 96
0
50
100
Summary/ Take-Home Messages
Biacore/ biophysical assays have the potential to support the leadidentification and pharmaceutical development of biologics and otherscaffolds from hit to clinical trials
Biophysical analyses with their high accuracy and precision - compared tobiological assays - increasingly gain importance as surrogate potency, QC andCMC assays
The most critical issue to generate highly sensitive Biacore data withbiological samples was overcome by intensive buffer optimization
For therapeutic scaffolds Biacore could be used for a variety of analyticalcomparability and QC tasks
Biacore 8k
Acknowlegdement
Axel VaterSandra MahrSven KlussmannLukas BethgeSimone SellWerner PurschkeJohannes HoosKlaus BuchnerDirk ZboralskiLisa BauerKathrin SchindeleGabi AnlaufDörte VossmeyerStefan ZöllnerStefan VonhoffFrank SchwöbelDirk EulbergAnna KruschinskiMatthias Baumann
Xenia Freifrau von MaltzanXiaoxi ZhuHarm Hermsen
GermanyAnja Drescher
Uwe Bierfeund
Uwe Roder
Thomas Von Kaenel
U.S.Brian Lang
Michael Murphy
Paul Belcher
JapanMami Okudaira
Discussions, help and inspiration: Biacore colleagues & friendsMichael Schraeml, Roche, Penzberg
Christian Stegmann, Bayer Healthcare, Berlin
Amaury Fernandez, Bayer Healthcare, Berlin
Jörg Bomke, Merck KGaA, Frankfurt
Stefan Geschwindner, Astra-Zeneca, Sweden
GlobalFredrik Sundberg
SwedenRobert Karlsson
Markku Hämäläinen
Olof Karlsson
Liselotte Molander
I still love to see great new ideas, data and also weird results
…and thank you for your attention
IndiaVinutha Devendran
U.K.Tim Fagge
ChinaHelen He
Abstract and Contact
49
Beyond Measuring Affinity: Biacore to take center stage in orthogonal analytics for comparability exercisesand quality tasks of biologics.
Christian Maasch, Xendo, Berlin, GermanyUnderstanding the structure-function relationship of a biotech product is vital for its successful development. Awareness of this is important, and byselecting the right assays at the right time, it is possible to reduce significantly the costs and risks of the R&D process. The diversity of therapeutic biologicsand their complexity not only poses a challenge for robust manufacturing, but also comprehensive characterization of these molecules.
SurfacePlasmonResonance (SPR)-based methods (Biacore) have shown to combine robustness with high sensitivity, precision, and accuracy andsubstantially assisted the lead identification and characterization of biological and chemical compounds over the last decade. By recent SPR methoddevelopments, these assays gained increasing importance and acceptance to support pharmaceutical QC, CMC, bioanalytics and comparability studies.
By applying orthogonal methods and approaches for characterization, information regarding the structural, binding interactions and functional propertiesof the biological product are linked – providing a greater understanding and interpretation of the determined characteristics.
Herein we show examples and insights on how SPR with information-rich data could support the analysis of biologics in terms of Critical Quality Attributes(CQAs) and drug release testing, as well as for comparability exercises for the development of biosimilars.
Christian Maasch, XendoManaging Consultant Drug Development & RASiemensdamm 6213627 Berlintel +49 (0) 30 856 068 78 – 24mob +49 (0) 1511 0348793 email [email protected] [email protected]