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PARTICLES IN THE BIOTECH
PRODUCT LIFE CYCLE: ANALYSIS,
IDENTIFICATION AND CONTROL
Dr Tara Sanderson, Formulation Services ...
2
KEY MESSAGES
 Why is it important to characterise and control particles in
the product?
 What different types of parti...
3
WHY DO WE NEED TO CONTROL PARTICLE
LEVELS?
 Potential to cause immunogenic responses
 Regulators require demonstrable ...
4
WHAT IS THE IMPACT IF PARTICLE
GENERATION IS NOT CONTROLLED?
 Decreased shelf life and / or alternative storage has an
...
5
TYPES OF PARTICLES
 There are various types of particles that may be present in
biotech products
 Non-Proteinaceous:
...
6
TYPES OF PARTICLES
 Proteinacious aggregates: visible and subvisible
Particle Size Particle Nature
~>100µm Visible part...
7
COMPARISON OF DP PROTEINACEOUS VS
NON-PROTEINACEOUS PARTICLES
 Silicone oil particles from a syringe  Protein aggregat...
8
Fragments Monomer Oligomers Subvisible Particles Visible Particles
SEC / SEC/MALS
SV-AUC
LO / MFI
NATIVE-PAGE
DLS / Nano...
9
 Protein / protein interactions: electrostatic interactions /
hydrophobic interactions / covalent bonding from free thi...
10
• Control through
Sequence design:
Technologies
available for
evaluation of
aggregation
propensity
• Free thiols
Sequen...
11
CONTROL THROUGH FORMULATION –
CASE STUDY
Case Study: IgG1, pI 9.6, ~150 kDa, formulated in 20mM PO4, 125mM NaCl, pH.7
...
12
PREFORMULATION CHARACTERISATION
Analysis Control Degraded
Primary
structure:
NR Peptide
mapping-MS
for SS-
bridges
No s...
13
PREFORMULATION CHARACTERISATION
 Conformational stability: Intrinsic Fluorescence: 9-50µl, 96 well plate format
35
30
...
14
Oligomers
Analysis Control Degraded Material
consumption
Visual
appearance
Clear, colourless Opalescent,
colourless
0.5...
15
Analysis Control Degraded Material
consumption
DLS
Peak 1
Peak 2
Mean Radius (nm): 5.5
Mean MW: 182kDa
% Intensity: 100...
16
PREFORMULATION CHARACTERISATION
≥2µm ≥5µm ≥10µm ≥25µm ≥50µm ≥100µm
LO Untreated 1730 515 110 15 0 0
MFI Untreated 12378...
17
CONCLUSIONS FROM THE PREFORMULATION
CHARACTERISATION
 Conclusions from the preformulation characterisation:
 Aggregat...
18
PH SCREEN
Buffers salts and excipients selected based on the
the et the route of administration and degradation profile...
19
EXCIPIENT SCREEN
From pH Screen: 25mM succinate, 125mM NaCl, pH6.5
Design Factors for DOE:
 2 % Trehalose
 His, Pro, ...
20
RESULTS FROM EXCIPIENT SCREEN
SEC
DLS (60°C)
21
LEAD CANDIDATE SELECTION AND ANALYSIS
DOE Lead candidates selection:
 R25: 67mM Gly, 67mM Arg, 0.01% Tween 20, 0.05%
P...
22
Tm2 Fab
Tm1 Fc, CH2
Tm3 Fc, CH3
DSC Thermograms
LEAD CANDIDATE SELECTION
23
LEAD CANDIDATE SELECTION
(TM ONSET DATA )
Optimal candidates from DSC
Ranking:
1: R25
2: R25b
3: R38
4: R30
24
LEAD CANDIDATE SELECTION:
INTRINSIC FLUORESCENCE & SLS: OPTIM 2 WITH HEAT RAMP
FROM 20-95°C
Tm1
Tm2
Tagg 266nm
Tagg 473...
25
LEAD CANDIDATE SELECTION:
PARTICLE COUNT RESULTS
26
Ranking from Conformational Analyses:
 1 R25: 67mM Gly, 67mM Arg, 0.01% Tween 20, 0.05% Poloxamer 188
 2 R25b: 67mM G...
27
SUMMARY
 Traditional screening tools, such as SEC & DSC are useful
methods to employ in a formulation screen, but it i...
28
ACKNOWLEDGEMENTS
SGS M-Scan Formulation and Biophysical team:
 Aoife Bolger
 Marisa Barnard
 Inigo Rodriguez-Mendiet...
29
Life Science Services Dr. Tara Sanderson
Formulation Services Manager
SGS M-Scan Ldt Phone: +44 (0)118 989 6940
Berlin ...
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Particles in the Biotech Product Life Cycle: Analysis, Identification and Control

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This presentation looks at the different technologies available for detection of particles generated during the drug development lifecycle and their control using a formulation approach for particles generated as a result of agitation and freeze/thaw, events commonly observed during sample shipment and temperature excursions.

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Transcript of "Particles in the Biotech Product Life Cycle: Analysis, Identification and Control"

  1. 1. PARTICLES IN THE BIOTECH PRODUCT LIFE CYCLE: ANALYSIS, IDENTIFICATION AND CONTROL Dr Tara Sanderson, Formulation Services Manager, SGS M-Scan
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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:
  6. 6. 6 TYPES OF PARTICLES  Proteinacious aggregates: visible and subvisible Particle Size Particle Nature ~>100µm Visible particles ~1-100µm Sub-visible particles >10nm – 1µm Oligomers
  7. 7. 7 COMPARISON OF DP PROTEINACEOUS VS NON-PROTEINACEOUS PARTICLES  Silicone oil particles from a syringe  Protein aggregation in DP vial
  8. 8. 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
  9. 9. 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?
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 13 PREFORMULATION CHARACTERISATION  Conformational stability: Intrinsic Fluorescence: 9-50µl, 96 well plate format 35 30 25 20 15 10 5 0 Intensity/10 3 counts 500450400350300250 Wavelength / nm Optim 2 -5000 0 5000 10000 15000 20000 25000 30000 35000 40000 320 370 420 Fluorescenceintensity(au) Wavelength (nm) untreated treated Clariostar BCM, Barycentric Mean
  14. 14. 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
  15. 15. 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
  16. 16. 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 NumberofParticlespermL Particle Size (µm) LO Untreated MFI Untreated LO Treated MFI Treated 6000 / container 600 / containerUSP<788>
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 20 RESULTS FROM EXCIPIENT SCREEN SEC DLS (60°C)
  21. 21. 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
  22. 22. 22 Tm2 Fab Tm1 Fc, CH2 Tm3 Fc, CH3 DSC Thermograms LEAD CANDIDATE SELECTION
  23. 23. 23 LEAD CANDIDATE SELECTION (TM ONSET DATA ) Optimal candidates from DSC Ranking: 1: R25 2: R25b 3: R38 4: R30
  24. 24. 24 LEAD CANDIDATE SELECTION: INTRINSIC FLUORESCENCE & SLS: OPTIM 2 WITH HEAT RAMP FROM 20-95°C Tm1 Tm2 Tagg 266nm Tagg 473nm
  25. 25. 25 LEAD CANDIDATE SELECTION: PARTICLE COUNT RESULTS
  26. 26. 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
  27. 27. 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.
  28. 28. 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
  29. 29. 29 Life Science Services Dr. Tara Sanderson Formulation Services Manager SGS M-Scan Ldt Phone: +44 (0)118 989 6940 Berlin & Taunusstein E-mail : tara.sanderson.@sgs.com Web : www.sgs.com/lifescience THANK YOU FOR YOUR ATTENTION
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