the development of biophysical screening methods to ... · development of an antibody are...
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Company Confidential 2012 © Eli Lilly and Company 1
Bryan Jones, Ph.D. Sr. Research Advisor BioTechnology Discovery Research Lilly Biotechnology Center
The development of
biophysical screening
methods to identify better
candidates
Eli Lilly and Company 2 Lilly Biotechnology
What do I mean by “better?”
Many issues that hinder or prevent
development of an antibody are
fundamentally biochemical properties
of the antibody itself:
• Chemical properties – e.g. chemical
degradation
• Physical properties – e.g. solubility,
physical stability, viscosity
• Binding properties: selectivity (or lack
thereof), self-association, “stickiness”
All of which can impact the ability to
manufacture, formulate, and
deliver a safe and efficacious drug
for the patient
The question then becomes:
Can we identify problem
molecules sufficiently early
in the process such that they
can either be avoided, or
“fixed?”
And if so, how?
Molecules that possess not only the appropriate biology, but also appropriate biochemical and biophysical properties that are suitable for manufacture, formulation, and ultimately delivery to the patient
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Diverse sources of antibodies
Ideally, we should develop processes that can effectively identify the best candidates for clinical development: • Provides appropriate biological and biochemical characterization • Take full advantage of the available diversity
• Discovery • Humanization
• Biological char. • Engineering
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• Discovery• Humanization
• Biological char.• Engineering
From a biochemistry perspective,
diversity isn’t always a good thing…
1000s 1 # of Molecules
100s of mgs to grams
µgs Amount of material
The challenge becomes how to provide useful information about the biochemical & biophysical properties of molecules that impact large quantities and high concentration
Computational approaches
Higher-throughput measurement of useful/relevant
parameters High-throughput, small-scale, predictive
approaches
…without having either available.
Eli Lilly and Company 5 Lilly Biotechnology
In Silico “characterization” screening
Novartis & MIT
Pfizer
Pharm Res. 2014 May
Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior
in terms of molecular properties.
PNAS 2014 Nov 17
In silico selection of therapeutic antibodies for development: Viscosity, clearance, and chemical
stability
Genentech
J Pharm Sci. 2012 Jan
Developability index: a rapid in silico tool for the screening of antibody aggregation propensity
MABs 2015 March
Aggregation risk prediction for antibodies and its application to biotherapeutic development
Lonza Biologics
MABs 2015
Computational tool for the early screening of monoclonal antibodies for their viscosities
MIT & Novartis/Pfizer/MedImmune)
Recently published examples of computational approaches
• Material-free (but computing intensive) • Can be an “initial filter” • Only requires sequences • Automated processing
Batch submission for modeling: fasta files of Vh, Vk
Structure preparation
Rank and predict “developability” based on several key parameters
(SAP, charge, dipole, etc)
Calculate SAP, other biochemical/biophysical
properties
Generate database with properties
Throughput: 10’s-100’s of mabs/day
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Computational approaches aren’t ideal,
but they help…
• Some things are easier to predict
than others (e.g. total charge, vs.
charged surfaces), but generally
can identify poor-behaving outliers
• Our experience using published
methods has been variable,
possibly because of:
1. Limited / unique training sets used for
model development
2. Commonly occurring differences in
molecular attributes (e.g. Fc isotype)
Therefore build model(s) trained by our data (knowledge/experience), fit to our
needs and processes, and using a two pronged modeling approach (MOA-based &
machine training)
PK issues related with Non-specific binding (e.g. Genentech
publication)
HMW formation prediction based
upon DI calculation
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Extracting biochemical information from
small-scale, unpurified samples
• Quick, automated processing of samples
• Ability to retain affinity purified (neutralized) material for further testing
• Obtain titer, purified material and analytical data from 450ul of CCM without
analyst intervention. ~48 samples per 24 hours
• Sample compartment is cooled and covered to maintain stability of
collected, purified sample fractions.
• Ability to use vials or 96 well plate formats
• Multiple methods used:
• ProtA/G + aSEC (BEH200) + fraction collection/neutralization
• ProtA/G + RP (C18) of collected, reduced fractions
• ProtA/G + aHIC + fraction collection/neutralization (for MS analysis)
CCM
Collected Fractions
Protein G Calculate titer
1st Dimension Protein G
CCM from small scale transient transfections
(2-4 mL)
2nd Dimension RP
Thermo-fisher / Dionex
3.37 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.12
-1.5
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
60.0
65.0
70.0
75.0
80.0
85.0
93.2mAU
min
2423222120191817161514131211109876543211 - 4.448
2 - 5.494
3 - 6.462
WVL:280 nm
aSEC example - can extract: • % HMW • Retention time & peak width
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What should we do when we have
purified protein?
Primary goal is to have a general understanding of the biophysical properties of Mabs/BisMabs (using predictive assays) under both process and formulation conditions before beginning the
development of either…
Practical “Formulation” design
space
Practical “Purification
process” design space
Other conditions of interest for
comparative purposes
Complete “Biophysical Profile”
of the molecule
Meta-analysis and continuous improvement
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Probes of protein conformational stability Type of measurement Measured parameter Experimental method
Equilibrium thermal unfolding Thermal unfolding mid-point, Tm Fluorescence, differential scanning calorimetry (DSC)
Equilibrium denaturant unfolding Denaturant unfolding midpoint, D1/2 Fluorescence,
circular dichroism (CD)
Time-dependent thermal unfolding Thermal unfolding rate(s), KT unfolding Fluorescence, CD
Time-dependent denaturant unfolding Denaturant unfolding rates, KD unfolding Fluorescence, CD
Probes of protein colloidal stability Type of measurement Measured parameter Experimental method
Protein-protein interaction Second virial coefficient, A2 Static light scattering, self-interaction chromatography
Protein solubility by precipitation Precipitation midpoint, [precipitant] Ammonium sulfate or polyethylene-glycol precipitation with
static light scattering, turbidity
Protein diffusion interaction Diffusion interaction parameter, kD Dynamic light scattering
Probes of combined colloidal and conformational stability Type of measurement Measured parameter Experimental method
Thermal scanning aggregation Aggregation onset temp., temperature at
which aggregation rate exceeds a certain
value
Static light scattering, size-exclusion chromatography–high-
performance liquid chromatography (SEC–HPLC)
Time-dependent, thermally induced
aggregation
Aggregation rate(s) Static light scattering, turbidity, SEC–HPLC
Table 1: Measurements with the potential to predict long-term storage stability (Adapted from http://www.pharmtech.com/print/200434?page=full)
And by what methods?
Which of these techniques possess the right attributes? • Do not require large quantities of protein • Suitable for automation (simultaneous/nearly simultaneous
characterization of multiple proteins) • Behavior at low concentration that is predictive of high-concentration
behavior
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MabA
MabB
[(NH4)2SO4]
pH
Accelerated precipitation & physical
stability
Variables that drive saturation:
Precipitants: PEGs, Ammonium Sulfate
Buffers: pH range (3-10)
Salts: NaCl
Temperatures: 4, 25 C
10
Gibson, et al., J. Pharm Sci 2011
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Fully automated solubility ranking
Eight buffer conditions (varying buffer type, pH, [Salt])
PEG 4K as precipitant (one antibody per 96w plate)
Sample requirement = 2.5-3.0mg (@1mg/mL)
• Automated liquid handling (Biomek FX) to transfer Mabs, dilute into
buffer/PEG solutions, transfer to 384 well plate
• Turbidity (A350) measured
1 0 2 0 3 0
0
2
4
6
8
L A 4 9 8
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
L A 3 0 7
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
L A 4 7 4
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
A M E 1 3 3
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
L A 4 8 0
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
L A 4 8 8
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
L A 2 9 4
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
1 0 2 0 3 0
0
2
4
6
8
L A 4 4 3
% P E G 4 K
A3
50
A 5
C 6
C 6N
H 6
P 7 .4
A 5 + S O 4
A 5 + N a C l
B 8 .1
Automated data analysis
LA307 LA474 LA426 LA488 LA480 LA424 LA475 LA463 LA294 LA443 AME133 LA498
B1 (A5) 26 26 20 26 26 26 26 14 26 26 26 26
B2 (C6) 10 4 14 18 14 14 10 14 16 14 18 16
B3 (C6N) 20 12 20 24 18 18 16 22 20 20 24 22
B4 (H6) 12 18 6 26 18 22 20 6 26 26 26 20
B6 (P7.4N) 18 10 18 24 18 18 16 22 18 18 24 20
B10 (NaOAc/SO4, pH5) 26 26 22 20 22 16 14 12 20 26 26 26
B11 (NaOAc/0.2M NaCl pH5) 20 18 18 26 22 20 18 18 18 26 26 20
B12 (Bicine/NaCl pH8.1) 6 6 12 18 10 10 10 16 14 10 20 16
Bu
ffe
r
Mab
Mab 1 Mab 2 Mab 3
Mab 4 Mab 5 Mab 6
Mab 7 Mab 8 Mab 9
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Self-association by light-scattering
3.0E-07
3.5E-07
4.0E-07
4.5E-07
5.0E-07
5.5E-07
6.0E-07
6.5E-07
0 2 4 6 8 10 12Dif
fusi
on
Co
effi
cie
nt
(cm
2/s
)
Antibody Concentration (mg/mL)
Interaction parameter (Kdiff), measured by DLS, has been extensively used to predict high concentration behavior of antibodies (e.g. viscosity, solubility)
100 mg/mL, 1 wk, 4 °C
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Automated DLS/Kdiff Determination
• 5-point dilutions (in duplicates) ,
requires 4 mg Mab per buffer
condition
• 96w source plate, dilutions
prepared in 384w plate
• Adds mineral oil overlay
Buffer LA307 LA474 LA426 LA488 LA480 LA424 LA475 LA463 LA294 LA443
B1 (A5) -34.4 -30.0 -5.8 -1.0 -0.5 -10.0 19.2 -21.5 14.2 29.8
B2 (C6) -22.7 -15.0 -17.8 -20.5 -5.4 -21.7 -15.3 13.3
B3 (C6N) -23.9 -56.7 -14.1 -7.0 -10.5 -10.5 -8.1 -5.8 -9.8 -0.1
B4 (H6) -42.5 -20.4 -14.5 -21.8 -23.3 7.8 14.1 9.1
B5 (H6N) -25.1 -45.1 -15.2 -8.0 -9.7 -14.1 -6.4 -7.2 -7.8 -2.7
B6 (P7.4N) -26.3 -35.5 -16.8 -7.9 -11.1 -7.9 -13.9 -0.6 -10.1 -4.7
B7 (NaOAc/Cit pH3.3) 16.5 34.7 32.2 39.5 36.7 35.5 37.6 22.3 36.0 31.5
B8 (NaOAc/Cit/Tris pH5) -36.3 -53.1 -10.2 -13.8 -13.2 -13.5 -11.5 -14.0 -10.2
B9 (NaOAc/Cit/Tris pH8) -66.7 -10.1 -12.9 -8.0 -14.3 -1.3 -9.2 -15.3
B10 (NaOAc/SO4, pH5) -26.1 -31.3 -9.5 -9.4 -6.1 -4.4 -9.5 -2.7 -5.0
B11 (NaOAc/0.2M NaCl pH5) -32.0 -33.8 -15.4 -5.0 -4.6 -6.3 -4.2 -9.6 -7.9 -2.7
B12 (Bicine/NaCl pH8.1) -10.9 -18.0 -11.5 -18.7 -11.7 -11.3 -16.1
Bu
ffe
r
Mab
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Moving towards a screening-based
approach to candidate identification
• Collectively, these tools
allow for the ID /
elimination of poor-
behaving Mabs before
investing significant effort
in their generation &
characterization
• These tools are sufficiently
high-through put to serve
as engineering screens
when needed
Drug
candidate
100’s
10’s
Few
Computational, 2D-chromatography
Solubility screen Self-association
# of molecules
Rigorous solubility & stability assessment
Mab identification
Antigen binding, cross reactivity
Cell-based activity
In vivo assessment (PK, disease models)
Simultaneous characterization of biological & chemical properties
Eli Lilly and Company 15 Lilly Biotechnology
Many Thanks to:
My entire group – AME Protein BioSciences
Especially:
• Qing Chai (“µ-Drop,” computational methods)
• Samantha Phan, Denisa Foster (2D-
chromatography)
• Jim Shih (automated methods for screening)