analytical similarity: lessons from the first us...

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.

2 | FDA/PQRI conference 2015, Bethesda

Agenda

Zarxio: analytical biosimilarity approach

Statistical tools for biosimilarity: considerations

Biosimilars – Development principles

Zarxio: biosimilarity assessment


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

* *


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


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!


Agenda






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


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


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


Tiered approach for analytical similarityassessment for a biosimilar

Source: CMC Strategy Forum Japan Dec 8, 2014, Marjorie Shapiro


Agenda






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


Both Zarxio and Neupogen have Very Low Levels of Aggregates, Oxidised and other Variants

13

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


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


Zarxio and Neupogen are Highly Similar Regarding All Molecular attributes

Source: FDA presentations for the January 7, 2015 Meeting of the Ocologic Advisory Committee


Agenda






Protein content as tier 1 quality attribute...



Statistical equivalence testing for protein content



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?


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


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


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


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


The practical limitations of equivalence testing

Undesired quality attributes:Less is better

More than one reference productpopulation

Biosimilar

Originator

Biosimilar

Originator


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


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


Acknowledgements

Thomas Stangler

Martin Schiestl

Jens Schletter

Jörg Windisch

Stefan Prasch

Hansjörg Toll

Franz Innerbichler

and many other colleagues from Sandoz Biopharmaceuticals

analytical similarity: lessons from the first us...

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