Late Stage Control Strategies in the Development of Vaccines and Biotherapeutics to Deliver Successful Commercial ProductsAparna Deora, Ph.D., Sr. Director, Analytical Research & Development
CASSS CPV FORUM, GAITHERSBURG, MD JULY, 2015
1
Pfizer’s Diverse Portfolio
Rare Disease
Inflammation& Remodeling
Oncology
Neuroscience & Pain
Immunoscience
Cardiovascular& Metabolism
Vaccine Research & Development
A diverse portfolio leads to a diverse range of molecular modalities
Diversity of Pfizer Commercial Biotech Products
GlycoconjugatesVirus-like ParticlesCarbohydratesmAb / mAb ConjugatesRecombinant ProteinsNative Proteins
Recombinant ProteinsConjugates
PeptidesOligonucleotidesHeparins
>500kDa
100-350kDa
20-100kDa
<20kDa
A common challenge within our industry is the establishment and maintenance of an efficient organizational structure and continuous implementation of state-of-the-art technologies that enable the compression of timelines and cost reduction (FTE’s and $$) while adhering to a robust CMC strategy.
BTx Pharm Sci is a Fully Integrated Organization Advancing Projects Across Five Sites
Andover, Massachusetts
Chesterfield, Missouri Cambridge, UK
S. San Francisco, CaliforniaPearl River, New York
>700 Colleagues
Scope of ResponsibilitiesAll large molecule Drug Substance, Drug Product, Analytical, Devices, and GCMC
Includes biosimilars, vaccines, cell and gene-based therapeutics
Support from molecular assessment and candidate selection through launch in major markets
Science Disciplines: Bioprocess R&D, Analytical R&D, Pharmaceutical R&D, Devices, GCMC
Close partnership with PGS (commercial manufacturing) for validation and Life Cycle management
Outline
• Background– Control Strategy Commercialization Continuous Process
Verification (CPV)
– Product and Process Understanding are key drivers in developing your Control Strategy
• Case Studies– Vaccines
– ADCs
– mAbs
• Summary
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What is a Control Strategy ICH Q10 definition:
“A planned set of controls, derived from current product and process understanding that ensures process performance and product quality. The controls can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control.”
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Pre-Clinical (Tox)
Early Clinical (Ph 1)
Late Clinical (Ph 2-3)
Validation PPQ Commercial
Leverage historical experiencePlatform Processes, Methods & SpecificationsBatch Records
Product knowledge and risk assessmentProcess Robustness, CPPranking & ValidationCQA ranking & SpecificationsMethod Validation
Continued Process & Method VerificationQuality SystemsAnnual Product Quality Review (APQR)
Control Strategy Lifecycle
7
Product/Process Understanding and Control are a Continuum
Pre-Clinical Early
Clinical (Ph 1)
Late Clinical (Ph 2-3)
Validation PPQ Commercial
Process Validation
Risk Assessment
Process Understanding
Experimental Plan
CQAsKey QAs
Product Understanding
Method Dev &Val
CPV
Control Strategy
Process Characterization
Studies
Product Characterization
Studies
ProbableCQAs
QTPP
Molecular Assessments
Increase Knowledge
Clinical Processes
• Platforms• Scale• Site
Challenges and Opportunities- Need to deliver a large and complex portfolio with compression of timelines and
cost reduction (FTE’s and $$) while maintaining quality and CMC strategy in place
- Balance early and late investments and risks
Commercial Product
Standard Development
Molecular Assessments and Platforms
Invest in Earlier Product and Process Characterization
Lock Your Process and Move to Commercial Site Early
Modality May Impact How You ApproachControl Strategy and CPV
Vaccines ADCs mAbs
Availability of Platform Processes and Toolboxes
Product and Process Complexity
11
VaccinesCase Study
Trumenba®; Bivalent rLP2086 Vaccine
Mechanism of action• fHBP/LP2086 binds human factor H &
protects bacteria from complement attack • Anti-LP2086 antibodies generated by
vaccine protect through inhibition of factor H binding allowing for complement bactericidal action
Two rLP2086 lipoproteins manufactured as separate drug substance materials• MW’s of both proteins ~28,000 Da• formulation includes PS80 which likely
results in micellar formation and an apparent MW of ~600,000-700,000 Da
Madico et al., 2006; Schneider et al., 2006; Mascioni et al., 2009; Seib et al., 2009; Ala’Aldeen et al., 2010; McNeil et al., 2009; Jacobsson, Mölling & Olcen, 2009
Final drug product• manufactured by mixing and diluting
the two rLP2086 proteins with stabilizer (aluminum phosphate)
Challenge: • Limited manufacturing experience leading to very tight lipid profile. • Limited data available on influence of fermentation process parameters on
lipid profiles.Resolution: Define operating space around manufacturing experience
Process Understanding: - Executed high resolution multifactor fermentation DOE using qualified scale down models and monitored lipid profiles as an output.
DS Process Characterization;Invest Early in Process Understanding Impact of bioreactor conditions on Lipid Isoform Profile
• Attribute: Lipid isoforms
– Lipid isoforms were identified early in development as a source of molecular heterogeneity
– Heightened characterization revealed O-acyl-linked lipids are critical to potency
• Process: Fermentation
– Early development data and literature review reveal potential link between bioreactor conditions and lipid isoform profile
DS CharacterizationInvest early in product understandingHeightened characterization of lipoproteins
Assess function of proteins
In Vitro Relative AntigenicityIn Vivo Potency
Native &Degraded
Assess function of lipids
In Vivo PotencyTLR2 Receptor Assay
Native &De-O-Acylated
Confirm Primary Structure of
lipoprotein and Identify the major and minor product
isoforms
Peptide level: Non-reduced peptide mapping of Lys-C proteolytic fragments by RP-UHPLC/UHR-ESI-Orbitrap MS
Intact level: Molecular mass determination for intact molecule by RP-UHPLC/UHR-ESI-QTOF MS
Confirm structure of lipids
GC/MS of released derivatized fatty acids
LC-MS of lipase digested lipoprotein
Lipid ProfileRP-HPLC for lipid isoform profile3 Major, 5 Minor
4MnB2086A-1002Degradants De-O-Acylated
A B C D E
1
23
A B C D E
A B C D E
A B C D E
A B C D E
1
23
12
3
12
3
1
23
4MnB2086A-1003
G10700B302
G10700B303
G10700B304
4MnB2086A-1002Degradants De-O-Acylated
A B C D E
1
23
A B C D E
A B C D E
A B C D E
A B C D E
1
23
12
3
12
3
1
23
4MnB2086A-1003
G10700B302
G10700B303
G10700B304
Site 2 pilot
Pilot Scale batch#1
Pilot Scale batch#2
Commercial Scale batch#1
Commercial Scale batch#2
Commercial Scale batch#3
DS Process CharacterizationLipid Peak Ratio: Peak 3
• Manufacturing batches run at center points
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Nor
mal
ized
Val
ue
Batch Number
Lipid Peak 3 Process Control
peak 3
Pk3 Prcs Cap:high
Pk3 Prcs Cap:low
DS Process CharacterizationLipid Peak Ratio: Peak 3
• Manufacturing batches run at center points
• Fermentation DOE data were used to define operating ranges at manufacturing scale to deliver consistent lipid profiles
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Nor
mal
ized
Val
ue
Batch Number
Lipid Peak 3 Process Control
peak 3
Pk3 Prcs Cap:high
Pk3 Prcs Cap:low
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ADCsCase Study
19
Case Study 2: Antibody Drug Conjugate (ADC)
Antibody:Monoclonal antibody (mAb) or related molecule (e.g. Fc fusion protein) specific to a cell surface tumor antigen/proteinAbundant target expression and internalization
Drug:Often highly potent small molecule drug/toxin with validated antitumor/cytotoxic mechanism of action (e.g. microtubule inhibition, DNA damage)
Linker:Tethers the drug/toxin to the antibodyStable in plasma, labile upon internalization to release drug
Clin Cancer Res 17, 6389-6397 (2011)
DS Process Characterization;Invest Early in Process Understanding Impact of conjugation conditions on DAR and distribution
• Attribute: Drug Distribution
– Understand how much drug and where drug is being conjugated to mAbsduring conjugation through lysine conjugation using calicheamicin-LP
• Process: Conjugation
Challenge: Understand the drug distribution across mAb with lysine conjugates Kinetically controlled “Random" versus site specificResolution: Build understanding across multiple programs to understand impact of mAb, LP and process
Process Understanding: For lysine conjugates need to understand impact of process and inputs on drug load and drug distributionPotential factors: Stochiometry, Solubilizers, Downstream Purification, etc.
Common ADC Conjugation Chemistries
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Cysteine conjugationLysine conjugation
Site-specific conjugationPotential impact of new technologies
(e.g site specific conjugation) Controlled drug loading, eliminate mixtures May improve in pharmaceutical properties May simplify analytics and development, and
present newer challenges
Junutula, J. R et al Nat Biotechnol, 26, 925-932 (2008)Strop, P, et al Chem Biol, 20, 161-167 (2013)
Strategies for Early Analytical Method Development Tool Box Approach
• Use platform method for mAbs where applicable• Build ADC analytical tool box
Pfizer Confidential │ 22
Tool Box for Attributes Linked to Conjugation
Attribute Methods
DAR UV, HIC, RP-HPLC, LC-MS
Drug Load Profile HIC, iCE, MS
Drug/RelatedImpurities ELISA, RP-HPLC
Un-conjugated mAb HIC, iCE
Potency Cytotoxicity assay
Conjugation Site/Occupancy
Peptide mapping by LC-MS
Lysine Chemistry
Example Tool Box for Drug Load Profile Determination of Lysine Conjugates
7.27
7
7.37
0
7.45
5
7.55
8
7.65
2
7.77
0
7.89
2
8.01
9
8.15
5
8.32
8
8.47
2
Abso
rban
ce
-0.010
0.000
0.010
0.020
0.030
0.040
0.050
0.060
0.070
0.080
0.090
0.100
0.110
0.120
pI6.80 6.90 7.00 7.10 7.20 7.30 7.40 7.50 7.60 7.70 7.80 7.90 8.00 8.10 8.20 8.30 8.40 8.50 8.60 8.70 8.80
D3
D4
D5D6D7
D8
iCE
23
D3
D4 D5
D6
D7D8D2D0
MS
24
Charge Heterogeneity by iCE- Lysine Chemistry
ADC showed more complicated charge heterogeneity than mAb
7.18
5 7.27
2
7.34
9
7.44
6
7.53
8
7.66
0
7.77
0
7.90
9
8.04
5
8.21
1
Abso
rban
ce
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
pI6.60 6.70 6.80 6.90 7.00 7.10 7.20 7.30 7.40 7.50 7.60 7.70 7.80 7.90 8.00 8.10 8.20 8.30 8.40 8.50 8.60
8.03
0
8.18
0
8.33
0
8.41
2
Abso
rban
ce
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
pI6.60 6.70 6.80 6.90 7.00 7.10 7.20 7.30 7.40 7.50 7.60 7.70 7.80 7.90 8.00 8.10 8.20 8.30 8.40 8.50 8.60
7.17
9 7.24
9
7.33
3
7.43
1
7.52
0
7.63
8
7.74
7
7.88
5
8.02
0
8.18
2
8.33
1
8.42
1
Abso
rban
ce
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
pI6.60 6.70 6.80 6.90 7.00 7.10 7.20 7.30 7.40 7.50 7.60 7.70 7.80 7.90 8.00 8.10 8.20 8.30 8.40 8.50 8.60
mAb + ADC
mAb
ADC
K39
3 +
AcB
ut
K33
5 +
AcB
ut
K24
7+ A
cBut
K29
1 +
AcB
ut
Sites of Conjugation in ADC1
25
• ~40 Lysines in sequence of mAb (80 in dimer)
• Four predominant sites in heavy chain (Fc) are the sites of conjugations
mAb1
ADC1
Exquisite control and consistency driven by process kinetics andmAb and calicheamicin properties
Sites of Conjugation Are Not Affected by Drug Input
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Low drug input
Mid drug input
High drug input
K39
3 +
AcB
ut
K33
5 +
AcB
ut
K24
7+ A
cBut
K29
1 +
AcB
ut
(from tryptic peptide map)
Similar sites of conjugation were observed with different Calicheamicin inputs
Similar Sites of Conjugation Are Observed in ADC2 and ADC3
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K24
2 +
AcB
ut
K33
0 +
AcB
ut
K28
6 +
AcB
ut
mAb2
ADC2
K24
9 +
AcB
ut
K39
5 +
AcB
ut
K33
7 +
AcB
ut
K29
3 +
AcB
ut
mAb3
ADC3
K38
8 +
AcB
ut
Summary of Results
• Pfizer mAb-calicheamicin process drives the site specificity of Lysine conjugate ADCs– Not correlated to
Calicheamicin Input– Independent of mAbs used
• The four sites seem to line up with the Fc pocket which is hydrophobic in nature
Pfizer Confidential │ 28
K290K334
K392K246
29
mAbs Case Study
Challenge: Platform monoclonal antibody ‘Force fit’ into mAb Platform process with significant yield loss and facility fit bottlenecks
Atypically high levels of high molecular mass species (HMMS) in cell culture observed during early development
Resolution: Leverage platform process understanding. React early when aytpical levels of aggregate seen in cell culture. Enhanced product understanding needed to enable a more robust product.
Case Study 3: Monoclonal AntibodyInvest Early in Process Understanding Build product understanding while leveraging platform knowledge
• Attribute: Aggregation– A known mAb CQA
– Analytical Toolbox for Aggregates
• Process: – Platform process
– Understand typical platform capability around control
•
Case Study 3: mAb Approach In a Platform Environment
• Background– Typical monoclonal antibody – Atypically high levels of high molecular mass species (HMMS) in cell
culture observed during early development
• HMMS Control Strategy – Early Clinical Phase– ‘Force fit’ into mAb Platform process with significant yield loss and
facility fit bottlenecks
• HMMS Control Strategy – Late Clinical Phase– Increased titers; unexpected increase of HMMS at large scale – Dramatic impact on process robustness, facility fit and overall
yield/manufacturability– Mechanistic investigation to understand the underlying cause of the
HMMS issue
Early Stage Control Strategy for HMMS relies on Low Yielding Platform Process
pH Inactivation
Harvest / ClarificationCentrifugation
Production Bioreactor~ 1.6 g/L
11-24% HMMS
MabSelect Protein A Chromatography
Planova 20 Viral FiltrationViral clearance
≤ 200 L/m2
Ultrafiltration/Diafiltration Final Concentration
AEX Weak Partitioning≤ 60 mg/mL Loading
HMMS < 3%
Final Formulation~100 mg/mL DS
HMMS < 3%
• Near platform process able to remove high level of HMMS to acceptable Phase I levels
• 24% HMMS in harvest reduced to < 3% in DS
• Acceptable process for Phase I• Issues for Late Stage Process
• Low process recovery (~55%)• Not suitable for commercial
manufacture due to low productivity and lack of process robustness
Focus on Product Understanding: Excess LC in the HMMS Enriched Sample
Sample Name % LC % HC %LC/%HCFully purified reference material 36.6% 63.4% 0.58
HMMS enriched sample 48.8% 51.2% 0.95
L Chain
L ChainH Chain
H ChainFully purified
reference material
HMMS enriched sample
HMMS Enriched Sample:Significant Amount of LC with N-terminal Extension
H Chain
AU
0.00
0.05
0.10
0.15
0.20
0.25
AU
0.00
0.05
0.10
0.15
Minutes10.00 15.00 20.00 25.00 30.00 35.00 40.00
Fully purified reference material
HMMS enriched sample
L Chain
L Chain
H Chain
-19SVP…ARCcm-L Chain-19Ac-SVP…ARCcm-L Chain
Full length signal peptide detected in ~50% of LC in HMMS enriched sample
Molecular design: Unexpected Involvement of a Full Length Signal Peptide
• Based on historical experience, the finding of uncleaved full length signal peptide 1 was unexpected.
• Could HMMS levels be lowered by using a different signal peptide?
Signal peptide 1 Framework 1
Framework 1
LC CDR1
LC CDR1Signal peptide 1
-20
-20
1
1
24
24
34
34
mAb X
mAb Y
Expected cleavage site
Identical aa sequence Identical aa sequence Different aa sequence
8 – 24% HMMS
~2% HMMS
Molecular Design
Alternate Signal Peptide Increased Expression by ~70% and Reduced HMMS to 2%
HMMS Dramatically Reduced with the Alternate Signal Peptide
AU
-0.002
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
Minutes5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00
HMMS by SE-HPLC
Sample from signal peptide 1
Sample from signal peptide 2
Late Stage Control Strategy
Theoretical process for high HMMS at harvest
Harvest/Clarification
MabSelect Protein A
AEX Chromatography
Polishing Column 2
~20-30% HMMS
10 cycles/batch
Theoretical HMMS 1 – 3%Theoretical yield ~30 – 40%
Final Formulation
9 cycles/batch
Polishing steps:Buffer volume: 3100 L/KgProcessing time: 65 hr
Eliminate 2nd Polishing step
Proposed process for low HMMS at
harvestHarvest/Clarification
MabSelect Protein A
AEX WP Chromatography
~2% HMMS
Potential HMMS <1%Potential yield ~70 – 80%
Final Formulation
3 cycles/batch
Polishing step:Buffer volume: 150 L/KgProcessing time: 12 hr
Reducing HMMS Level at Harvest can Mitigate Downstream Bottlenecks
Summary
Approach to building an effective control strategy throughout development across multiple modalities hinges on, Building Product Understanding
Building Process Understanding
Efficiency and Speed can be gained by, Leveraging product and process platform understanding Early investments in product and process understanding
Strategic planning
Final control strategy built to enable commercialization and implementation of a successful CPV strategy
Acknowledgements
• ARD: – PPL: April Xu, Jason Starkey, Heyi Li, Jim Mo, Jeff Borgmeyer,
Lawrence Chen, Jim Jiang, Bhumit Patel, Julius Lagliva, Tonya Matlosz, Eric Jin, Dan Boisvert and many more
– MSBC: James Carroll, Olga Friese, Lisa Marzilli
– BIT: Dave Cirelli– Laura Bass, Tom Porter, Jason Rouse, and Meg Ruesch
• BRD– Khurram Sunasara, Leo Letendre, Ranga Godavarti, Mary
Switzer, and Bo Arve
• BSO– Steffi Pluschkell
