Analytical similarity: Lessons from the first US biosimilar · 2015. 11. 3. · stimulates the...
Transcript of Analytical similarity: Lessons from the first US biosimilar · 2015. 11. 3. · stimulates the...
Analytical similarity: Lessons from the first US biosimilar2nd FDA/PQRI Conference on Advancing Product QualityOct. 5-7, 2015Corinna Sonderegger, Head Pharmaceutical DevelopmentSandoz Biopharmaceuticals, Austria
© 2015 Sandoz. All rights reserved. All trademarks are the property of their respective owners.
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Agenda
Zarxio: analytical biosimilarity approach
Statistical tools for biosimilarity: considerations
Biosimilars – Development principles
Zarxio: biosimilarity assessment
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Variability is inherent in biologics
Manufacturing changes
Manufacturing changes occur due to process improvements, scale up, etc Differences in attributes sometimes
significantly larger than batch-to-batch variability
Non-identicality is a normal principle in biologics
No batch of any biologic is “identical” to the other batches
Variability is natural even in the human body and usually not problematic
Batch-to-batch
0
10
20
30
40
50
60
02.2008 03.2009 05.2010 06.2011
Expiry date
G2F glycans[rel. area %]
0
10
20
30
40
50
60
07.2009 08.2010 09.2011
Basic variants[rel. area %]
Expiry date
Pre-shift
Post-shift
Pre-shiftPost-shift
Variability of major glycan variant in commercial mAB
* M. Schiestl et al. Acceptable Changes in Quality Attributes of Glycosylated Biopharmaceutical; Nature Biotechnology (2011) 29:310
* *
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Variability is inherent in biologics
Manufacturing changes
Manufacturing changes occur due to process improvements, scale up, etc Differences in attributes sometimes
significantly larger than batch-to-batch variability
Non-identicality is a normal principle in biologics
No batch of any biologic is “identical” to the other batches
Variability is natural even in the human body and usually not problematic
Batch-to-batch
0
10
20
30
40
50
60
02.2008 03.2009 05.2010 06.2011
Expiry date
G2F glycans[rel. area %]
0
10
20
30
40
50
60
07.2009 08.2010 09.2011
Basic variants[rel. area %]
Expiry date
Pre-shift
Post-shift
Pre-shiftPost-shift
Variability of major glycan variant in commercial mAB
* M. Schiestl et al. Acceptable Changes in Quality Attributes of Glycosylated Biopharmaceutical; Nature Biotechnology (2011) 29:310
* *
Safety and efficacy within this variability have been demonstrated in
clinical studies and by real-life experience with the reference product
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Purification process development
Bioprocess development
Recombinant cell line development
Drug product development
PK/PD
Preclinical
Biological characterization
Physicochemical characterization
Clinical
Reference product
variability
Processdevelopment
Analytics
3. Confirmation of biosimilarity
Biological variability
2. Target directed development
Target range
1. Target definition
Biosimilars are systematically developed to match the reference product
No clinically relevant differences!
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Agenda
Zarxio: analytical biosimilarity approach
Statistical tools for biosimilarity: considerations
Biosimilars – Development principles
Zarxio: biosimilarity assessment
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Overview of Zarxio® (filgrastim)
Zarxio is a biosimilar of the reference product Neupogen® (filgrastim)• Filgrastim (recombinant granulocyte-colony stimulating factor (G-CSF)), which
stimulates the proliferation of white blood cells• Was first approved in the EU in 20091 and subsequently developed for US
marketing authorization• Since approval, has become the volume leader in Europe• Received marketing authorization by FDA in March 2015 as first biosimilar approved
in USA; launched Sep. 2015 in USA
Zarxio US Development Program• Analytical: Battery of structure and function analyses• Nonclinical: 5 animal studies to assess PD, toxicity, toxicokinetics, and local
tolerance• Clinical (confirming similarity):
– 1 pivotal and 4 supportive PK/PD studies to demonstrate similar PK/PD– Comparative safety and efficacy clinical study
7
1 Marketed as “Zarzio®” ex-US
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Attributes e.g.: Primary structure
Mass Disulfide bridging Free cysteines Higher order
structure N- and C-terminal
heterogeneity Glycosylation Glycation
Fragmentation Oxidation Deamidation Aggregation Particles
Target-binding Fc effector
functions
Methods e.g.: MS
Peptide mapping Ellman‘s CGE
SDS-PAGE CD, FT-IR
H-D exchange NMR, X-ray HPLC HPAEC IEF
2AB NP-HPLC SE-HPLC FFF AUC DLS MALLS Bioassays SPR
Enabling technology: state-of-the-art analytics allow for thorough characterization of biologics
Combination of attributesMVDA, mathematical algorithms
PrimaryStructure Higher
OrderStructure
ChargeVariants
Biologicalfunctions
Glycosylation
Size variants
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CQA assessment for ZarxioWhat Matters for Filgrastim Safety and Efficacy
Quality Attribute Criticality Relevant for Methods Used
Amino acid sequence Very HighEfficacy, Safety, Immunogenicity Edman, peptide mapping, MS
Potency Very High Efficacy, Safety Bioassay
Target binding Very High Efficacy, Safety Surface plasmon resonance
Protein concentration Very High Efficacy Content determination
Higher order structure High Efficacy, Immunogenicity CD and NMR spectroscopy
High-Molecular Weight Variants/Aggregates High Immunogenicity Size exclusion chromatography
Oxidized Variants High Efficacy Reversed phase chromatography
Subvisible particles High Immunogenicity Light obscuration
Truncated Variants Low None RP-HPLC-MS
Norleucine Very Low None Reversed phase chromatography
Deamidation Very Low None Cation exchange chromatography
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Tiered approach for analytical similarityassessment for a biosimilar
Source: CMC Strategy Forum Japan Dec 8, 2014, Marjorie Shapiro
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Agenda
Zarxio: analytical biosimilarity approach
Statistical tools for biosimilarity: considerations
Biosimilars – Development principles
Zarxio: biosimilarity assessment
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Biosimilarity assessment Zarxio with NeupogenHighly similar prim., sec., tertiary structure
Primary structure (amino acid sequence): same for Zarxio and Neupogen• Edman Sequencing• Peptide Map • Mass Spectrometry• Amino Acid Analysis
Secondary structure: CD (circular dichroism) overlay
Tertiary structure: 2D NMR overlay
2,5 3,8 5,0 6,3 7,5 8,8 10,0 11,3 12,5 13,8 15,0 16,3 17,5 18,8 20,0 21,3 22,5 23,8 25,0 26,3 27,5 28,8 30,0 31,3 32,5 33,8 35,0 37,00
50
100
150
200
250
300
350
400
450
500 mV
min
G2+G
3
G6
G8
G7
G4 ‐S‐S‐
G12
G10
G11‐2a
G11‐2b G9
+G10
blank pe
ak
G11
G1G8
‐G10
G7‐G10
G5 ‐S
‐S‐
Zarxio® #3
Zarxio® #2
Zarxio® #1
Neupogen® US #1
Neupogen® US #2
Neupogen® EU
ZarxioNeupogen
Biosimilarity tool: Qualitative / Visual comparison
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Both Zarxio and Neupogen have Very Low Levels of Aggregates, Oxidised and other Variants
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Size Exclusion HPLC (SEC)
Source graph: Sandoz
Zarxio® #3Zarxio® #2Zarxio® #1
Neupogen® US #1Neupogen® US #2
Neupogen® EUAggregates Dimer
Oligomers
10,0 11,0 12,0 13,0 14,0 15,0 16,0 17,0 18,0 19,0 20,0 21,0 22,0 23,0 24,0 25,250,3
52,0
54,0
56,0
58,0
60,0
62,0
64,0
66,0
68,0
70,9mV
min
Reversed Phase HPLC (RPC)
Biosimilarity tool: Quantitative comparison Min-max or equal/less than reference product
High resolution, high sensitivity methods
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Sensitive Biological Assay Confirms Highly Similar Biological Activity
Product Zarxio NeupogenNeupogen
Product InformationSpecific activity in U/mg x 108 1.0 – 1.1 0.9 – 1.2 0.4 – 1.6
14Source image: Sandoz
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Zarxio and Neupogen are Highly Similar Regarding All Molecular attributes
Source: FDA presentations for the January 7, 2015 Meeting of the Ocologic Advisory Committee
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Agenda
Zarxio: analytical biosimilarity approach
Statistical tools for biosimilarity: considerations
Biosimilars – Development principles
Zarxio: biosimilarity assessment
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Protein content as tier 1 quality attribute...
Source: FDA presentations for the January 7, 2015 Meeting of the Ocologic Advisory Committee
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Statistical equivalence testing for protein content
Source: FDA presentations for the January 7, 2015 Meeting of the Ocologic Advisory Committee
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Why statistics...?
Statistics could have a potential to...
Describe large and complex sets of data in a clear manner (many QAs, different outputs, different criticality)
Deduce hard facts out of “soft” data
Develop an objective basis for decisions (define the statistical hypothesis which can support a biosimilar decision – link to the scientific understanding)
Support the background of a decision in a convincing and traceable manner
How much of this is actually applicable in real life?
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Selecting the statistical tool to compare populations
Source: adapted from FDA presentations for the January 7, 2015 Meeting of the Ocologic Advisory Committee
Qua
lity
Attr
ibut
e
Raw data originator
Quality Ranges
Min/Max 3 StdevToleranceInterval(90/99)
Raw data biosimilar
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Equivalence testing to assess a practical differencein the means
µ1
µ2
0
The EAC interrelates strongly to sample sizes, allowable difference (δ =µ1-µ2), significance level (α) and power (1-β)
Concluding equivalence by rejecting the null hypothesis H:|µ1-µ2|≥δ
means & sample sizes
n1
n2
Confidence intervalfor the difference of the means
Equivalence AcceptanceCriterion (EAC)
Population to be evaluated
Comparator population
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The conceptual & theoretical flaws of equivalencetesting
All biosimilar batches arewithin variability of originator
means are different not equivalent
Some biosimilar batches areoutside of the variability oforiginator
means are the same equivalent
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The practical limitations of equivalence testingVery low sample sizes& analytical variability Non-normal distributions
Biosimilar
Originator
Biosimilar
Originator
• Actual distributions are rarely normal, normality is often only a coarse approximation
• Special cause variability in manufacturingprocesses: deviations, changes, raw material variability
• A biosimilar program may not requiremore than 5 – 10 large scale batches
• Disproportionate costs – little scientificvalue – for statistical reasons only
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The practical limitations of equivalence testing
Undesired quality attributes:Less is better
More than one reference productpopulation
Biosimilar
Originator
Biosimilar
Originator
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Final thoughts on statistical tools for comparability / biosimilarity evaluation...
First be clear about your scientific question, then choose the statistical tool, and be aware of the limitations
The statistical test parameter needs to be carefully chosen• Too tight: a product may be statistically not comparable to itself• Too loose: a non-comparable product may fit it
Statistics should not be a self-contained claim for biosimilarity on the quality level, it always should be just a contributor to the totality of evidence
Statistical tools may be able to flag those quality attributes which need further evaluation• May speed up final evaluation if statistics is set up in the right way• However, the incremental knowledge gain is very little compared to
a descriptive but critical raw data comparison
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Zarxio analytical biosimilarity learnings
Assess analytical similarity considering
• Extensive data package of reference product and biosimilar: set of methods, number of batches
• Quality attribute criticality assessment of impact on safety & efficacy as well as uncertainty
• Tiered approach: • Select proper tool(s) to assess similarity, including
statistics, min-max, graphical comparison
Statistics as one tool, supportive, but not as exclusive tool Align with agency early on analytical similarity approach
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Acknowledgements
Thomas Stangler
Martin Schiestl
Jens Schletter
Jörg Windisch
Stefan Prasch
Hansjörg Toll
Franz Innerbichler
and many other colleagues from Sandoz Biopharmaceuticals