Particles in the Biotech Product Life Cycle: Analysis, Identification and Control
-
Upload
sgs -
Category
Healthcare
-
view
901 -
download
0
description
Transcript of Particles in the Biotech Product Life Cycle: Analysis, Identification and Control
![Page 1: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/1.jpg)
PARTICLES IN THE BIOTECH
PRODUCT LIFE CYCLE: ANALYSIS,
IDENTIFICATION AND CONTROL
Dr Tara Sanderson, Formulation Services Manager, SGS M-Scan
![Page 2: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/2.jpg)
2
KEY MESSAGES
Why is it important to characterise and control particles in
the product?
What different types of particles are often seen in the
product?
Summary of mechanisms of proteinaceous particle
generation
Overview of instrumentation useful for particle analysis
Higher risk areas of particle generation in a drug
development program and routes of control
Case study: Reformulation of a mAb showing significant
aggregation following shipment and temperature
excursions – useful HTS techniques to incorporate
![Page 3: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/3.jpg)
3
WHY DO WE NEED TO CONTROL PARTICLE
LEVELS?
Potential to cause immunogenic responses
Regulators require demonstrable limitation, control and
identification of product-related impurities
Can impact product stability and shelf life
![Page 4: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/4.jpg)
4
WHAT IS THE IMPACT IF PARTICLE
GENERATION IS NOT CONTROLLED?
Decreased shelf life and / or alternative storage has an
overall impact on cost and profitability of the drug product
Regulators will require further characterisation and
evidence of clearance
If aggregation is significant, process changes or
reformulation may be required - Time and cost
implications
Following reformulation, comparability studies are required to
determine impact on continued use of reference standard and
suitability of method validations
Significant time and cost impacts if method validations need
repeating or new Ref Std required
Additional batches / new stability studies required
![Page 5: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/5.jpg)
5
TYPES OF PARTICLES
There are various types of particles that may be present in
biotech products
Non-Proteinaceous:
Fibres: e.g. container closure shards, shedding from filters
Particulates that shed from packaging: glass / plastics
Delamination: Plastic: Rubber:
Silicone oil from syringes:
![Page 6: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/6.jpg)
6
TYPES OF PARTICLES
Proteinacious aggregates: visible and subvisible
Particle Size Particle Nature
~>100µm Visible particles
~1-100µm Sub-visible particles
>10nm – 1µm Oligomers
![Page 7: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/7.jpg)
7
COMPARISON OF DP PROTEINACEOUS VS
NON-PROTEINACEOUS PARTICLES
Silicone oil particles from a syringe Protein aggregation in DP vial
![Page 8: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/8.jpg)
8
Fragments Monomer Oligomers Subvisible Particles Visible Particles
SEC / SEC/MALS
SV-AUC
LO / MFI
NATIVE-PAGE
DLS / Nanoparticle Tracking
Analysis (Nanosight)
AF4
Visual Appearance
Resonant Mass Measurement
ANALYSIS OF PARTICLES
1mn 10mn 100nm 1µm 10µm 50µm >100µm
![Page 9: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/9.jpg)
9
Protein / protein interactions: electrostatic interactions /
hydrophobic interactions / covalent bonding from free thiols
or exposed internal thiols
Air / liquid interface / container interactions: partial
unfolding of the molecule
Protein / contaminant interactions: critical nucleus –
catalyst for aggregation formation
IN MOST CASES AGGREGATION EVENTS OCCUR AS A RESULT OF
PARTIAL CONFORMATIONAL CHANGES
MECHANISMS BEHIND PARTICLE FORMATION?
![Page 10: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/10.jpg)
10
• Control through Sequence design: Technologies available for evaluation of aggregation propensity
• Free thiols
Sequence • Low pH hold
• Filtration / column selection
• Include in-process aggregate analysis
Expression and
Purification
• Inadequate formulation design: Ensure aggregation assessed upon agitation and F/T
Formulation
• Include continued sub-visible particle testing as part of characterisation & comparability studies
• Reformulate
Characterisation
• Agitation of liquids
• Ensure shipment studies and excursions studies completed: alternative condition
• Reformulate
Shipments
• Thawing may
show particles – ensure before and after tests performed
• Filter before fill
• Reformulate
Drug Product Fill
• Route of administration: Assess with in-use studies
• Reformulate
Release
• Measure particle trends
• Characterise any particles generated
• Reformulate
Stability Studies
POTENTIAL ROUTES FOR AGGREGATION &
CONTROL
![Page 11: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/11.jpg)
11
CONTROL THROUGH FORMULATION –
CASE STUDY
Case Study: IgG1, pI 9.6, ~150 kDa, formulated in 20mM PO4, 125mM NaCl, pH.7
IgG1 candidate was found to have higher than specification aggregation upon shipment and F/T
Challenges: Time and material constraints
Aim: To reformulate to control aggregation during shipment and potential temperature excursions
Formulation Design Strategy:
Employ preformulation characterisation on control and agitated material to determine degradation pathway and choose required methods for screening approach
Employ pH screen / followed by excipient screen using agitation and F/T degradation to define the optimum formulation
Sample Treatment
To mimic problem: Samples were degraded using conditions equivalent to the worst case shipment and temperature excursions that could be observed for the product-specific shipment route:
24h agitation at ambient / 3 x cycles in thermal cycling unit from -20°C to 40°C.
Degraded protein compared to control protein
![Page 12: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/12.jpg)
12
PREFORMULATION CHARACTERISATION
Analysis Control Degraded
Primary
structure:
NR Peptide
mapping-MS
for SS-
bridges
No scrambling
observed,
expected IgG1 SS-
bridge pattern
SS-bridge scrambling
observed
Charge
profile: icIEF
pI 9.3-9.6, 6
isoforms
pI 9.3-9.6, 6 isoforms
Equivalent profile to native
Secondary
structure:
FTIR
α-helix: 0%
β-sheet: 42%
α-helix: 0%
β-sheet: 43%
Equivalent profile
to control
Overall
tertiary
structure:
Near-UV CD
Equivalent profile
to degraded
Equivalent profile to
control, but some
differences observed
~ 280nm
![Page 13: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/13.jpg)
13
PREFORMULATION CHARACTERISATION
Conformational stability: Intrinsic Fluorescence: 9-50µl, 96 well plate format
35
30
25
20
15
10
5
0
Inte
nsity /
10
3 c
oun
ts
500450400350300250
Wavelength / nm
Optim 2
-5000
0
5000
10000
15000
20000
25000
30000
35000
40000
320 370 420
Flu
ore
scen
ce in
ten
sit
y (
au
)
Wavelength (nm)
untreated
treated
Clariostar BCM, Barycentric Mean
![Page 14: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/14.jpg)
14
Oligomers
Analysis Control Degraded Material
consumption
Visual
appearance
Clear, colourless Opalescent,
colourless
0.5mL
SE-UPLC Monomer: 98.7%
Aggregate: 1.2%
Fragment: 0.2%
Monomer: 95.1%
Aggregate: 1.5%
Fragment: 3.4%
5 µg
96 well plate format
SV-AUC Monomer: 82.2%
Dimer: 6.5%
Trimer: 5.8%
Pentamer: 2.1%
Hexamer: 3.4%
Monomer: 80.1%
Dimer: 7.6%
Trimer: 5.2%
Pentamer: 3.3%
Hexamer: 3.8%
40 µL
1mg/mL at 400µL
PREFORMULATION CHARACTERISATION:
AGGREGATION
AU
0.000
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.010
0.011
0.012
Minutes
4.20 4.40 4.60 4.80 5.00 5.20 5.40 5.60 5.80 6.00 6.20 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 8.00 8.20 8.40 8.60 8.80 9.00 9.20 9.40 9.60 9.80 10.00 10.20 10.40 10.60
Aggregates
Fragments
280 nm Monomer
![Page 15: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/15.jpg)
15
Analysis Control Degraded Material
consumption
DLS
Peak 1
Peak 2
Mean Radius (nm): 5.5
Mean MW: 182kDa
% Intensity: 100%
ND
Mean Radius (nm): 2.7
Mean MW: 34kDa
% Intensity: 34.7%
Mean Radius (nm): 34.4
Mean MW: 13,248kDa
% Intensity: 65.3%
20µl
384 well plate format
PREFORMULATION CHARACTERISATION
Control Aggregated Control Aggregated
![Page 16: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/16.jpg)
16
PREFORMULATION CHARACTERISATION
≥2µm ≥5µm ≥10µm ≥25µm ≥50µm ≥100µm
LO Untreated 1730 515 110 15 0 0
MFI Untreated 12378 1486 187 31 0 0
LO Treated 27525 25080 19565 5340 635 10
MFI Treated 61224 61661 30396 3042 363 25
0
10000
20000
30000
40000
50000
60000
70000 N
um
ber
of
Part
icle
s p
er
mL
Particle Size (µm)
LO Untreated
MFI Untreated
LO Treated
MFI Treated
6000 / container 600 / container USP<788>
![Page 17: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/17.jpg)
17
CONCLUSIONS FROM THE PREFORMULATION
CHARACTERISATION
Conclusions from the preformulation characterisation:
Aggregation – irreversible SS-bridge scrambling occuring
but no apparent charge based changes (deamidation /
oxidation)
No significant changes to 2°, minimal 3° structure or
conformational structure changes detected
Significant changes in particle numbers, with the majority
observed higher than 2µm
Screening Tools:
SE-UPLC & DLS
In addition, for lead candidates: Particle counts, DSC,
Intrinsic fluorescence
![Page 18: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/18.jpg)
18
PH SCREEN
Buffers salts and excipients selected based on the the et the route of administration and degradation profile
pH screen: from pH 3.5 to 7.5
Buffer ions containing 125mM NaCl: citrate, acetate, glutamate, succinate, histidine (25mM)
Samples agitated and treated to 3 x F/T cycles to choose optimum pH and buffer salt.
SE-UPLC & DLS utilised: total material consumed: 160ul (1.6mg) / total preparation & screen time: 48h
Optimal pH and buffer ion: 25mM succinate, pH 6.5 containing 125mM NaCl
Excipient screen: excipients selected for conformational stability & surfactants to reduce surface charge interaction
![Page 19: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/19.jpg)
19
EXCIPIENT SCREEN
From pH Screen: 25mM succinate, 125mM NaCl, pH6.5
Design Factors for DOE:
2 % Trehalose
His, Pro, Glu, Arg, Gly: 0 – 67 mM
Tween 20, Poloxamer 188: 0.01% to 0.1%
44 combinations
Screened using SE-UPLC: 5µg / degraded sample,15h
analysis time
DLS with heat ramp from 20-60°C: 20 µl / undegraded
sample, 3h analysis time
![Page 20: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/20.jpg)
20
RESULTS FROM EXCIPIENT SCREEN
SEC
DLS (60°C)
![Page 21: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/21.jpg)
21
LEAD CANDIDATE SELECTION AND ANALYSIS
DOE Lead candidates selection:
R25: 67mM Gly, 67mM Arg, 0.01% Tween 20, 0.05% Poloxamer 188
R25b: 67mM Gly, 67mM Arg, 0.06% Tween 20
R30: 67mM Pro, 22mM Gln, 0.1% Poloxamer 188
R38: 67mM Pro, 22mM Gln, 67mM Gly, 0.1% Tween 20, 0.1% Pol188
Predictive analysis using undegraded material by intrinsic fluorescence and DSC for thermal and conformational stability
Particle counts for >2 µm particles
Temperature Ramps: 20°C – 100°C, domain Tm’s and Tm onset and Tagg compared
![Page 22: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/22.jpg)
22
Tm2 Fab
Tm1 Fc, CH2 Tm3 Fc, CH3
DSC Thermograms
LEAD CANDIDATE SELECTION
![Page 23: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/23.jpg)
23
LEAD CANDIDATE SELECTION
(TM ONSET DATA )
Optimal candidates from DSC
Ranking:
1: R25
2: R25b
3: R38
4: R30
![Page 24: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/24.jpg)
24
LEAD CANDIDATE SELECTION: INTRINSIC FLUORESCENCE & SLS: OPTIM 2 WITH HEAT RAMP
FROM 20-95°C
Tm1
Tm2
Tagg 266nm
Tagg 473nm
![Page 25: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/25.jpg)
25
LEAD CANDIDATE SELECTION:
PARTICLE COUNT RESULTS
![Page 26: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/26.jpg)
26
Ranking from Conformational Analyses:
1 R25: 67mM Gly, 67mM Arg, 0.01% Tween 20, 0.05% Poloxamer 188
2 R25b: 67mM Gly, 67mM Arg, 0.06% Tween 20
3 R38: 67mM Pro, 22mM Gln, 67mM Gly, 0.1% Tween 20, 0.1% Poloxamer 188
4 R30: 67mM Pro, 22mM Gln, 0.1% Poloxamer 188
Ranking from Particle Counts:
1 R25: 67mM Gly, 67mM Arg, 0.01% Tween 20, 0.05% Poloxamer 188
2 R25b: 67mM Gly, 67mM Arg, 0.06% Tween 20
3 R38: 67mM Pro, 22mM Gln, 67mM Gly, 0.1% Tween 20, 0.1% Poloxamer 188
4 R30: 67mM Pro, 22mM Gln, 0.1% Poloxamer 18
Final Selection: 25mM Succinate, 125mM NaCl, 67mM Gly, 67mM Arg, 0.01% Tween 20, 0.05% Poloxamer 188, pH 6.5
FINAL SELECTION
![Page 27: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/27.jpg)
27
SUMMARY
Traditional screening tools, such as SEC & DSC are useful
methods to employ in a formulation screen, but it is also
critical to ensure larger aggregates are also investigated in
combination with these.
It is also critical to use methods that allow analysis of the
full range of aggregates and subvisible particles otherwise
a significant degradation pathway may not be properly
evaluated.
Ever increasing constraints on material availability and
shorter time to decision point means that more sensitive /
high throughput instrumentation is required for effective
early screening.
![Page 28: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/28.jpg)
28
ACKNOWLEDGEMENTS
SGS M-Scan Formulation and Biophysical team:
Aoife Bolger
Marisa Barnard
Inigo Rodriguez-Mendieta
Zeb Younes
David Miles
Stella Chotou
Jon Phillips
Fabio Rossi
![Page 29: Particles in the Biotech Product Life Cycle: Analysis, Identification and Control](https://reader033.fdocuments.net/reader033/viewer/2022060108/554d6ba2b4c905f6388b578b/html5/thumbnails/29.jpg)
29
Life Science Services Dr. Tara Sanderson
Formulation Services Manager
SGS M-Scan Ldt Phone: +44 (0)118 989 6940
Berlin & Taunusstein
E-mail : [email protected]
Web : www.sgs.com/lifescience
THANK YOU FOR YOUR ATTENTION