Global Landscape in the Development
of Biological Products
Technical, Preclinical and Clinical Aspects
Antonio da Silva, Head Preclinical Development
ANVISA, Brasilia, 26 June 2013
a Novartis company
Agenda
1
Biologics & biosimilars: An overview
2
Technical development of biosimilars
3
Preclinical development of biosimilars
3
The case for a new paradigm in clinical development
2 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Biologics have revolutionized modern medicine – and
will continue to do so
 “Borrowed from nature”, very complex
 Highly specific and powerful medicines
 Treat serious diseases
DNA molecule
decoded
1950s
Genetic code
cracked
1960s
Basic
biotechnology
enabled
1970s
Commercial
biotech firms
founded
1980s
Leading biotech
brands emerge
Human
genome
Stem-cell
research
Gene therapy
1990s to today
Source: Company websites and annual reports / Note: All trademarks, logos and pictures are the property of the respective owner
3 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Today / future
As a result, biologics sales are expected to be ~USD
143 bn in 2013 and to grow to ~USD 190 bn by 2018
Global pharmaceutical market, 2008-2018
USD billion
CAGR (percent)
2008-2013 2013-18
521
454
Therapeutic
proteins1
Small
molecules
94
620
3
4
Key market characteristics
190
9
6
• Majority of current sales
143
from mega blockbusters
• Monoclonal antibodies
(mAbs) are largest and
fastest growing segment
360
378
2008
2013
1 Vaccines
430
1
3
• ~30% of industry pipeline
are biologics1
2018
not included
Source: Evaluate Pharma, Feb 2013; Sandoz analysis
Access to biologics is a growing issue around the world
Almost one-quarter of 46 European countries do not
provide access to biologics for arthritis1
Cancer patients twice as likely as general population
to go bankrupt a year after their diagnosis2
Canadian children with juvenile idiopathic arthritis may
not receive "standard" care because pediatric coverage
for biologic drugs is limited and inconsistent3
Only 50% of severe RA patients receive biologics
across EU5, US and Japan4
1
EULAR 2012: Annual Congress of the European League Against Rheumatism
2 Cancer diagnosis as a risk factor for personal bankruptcy, ASCO 2011
3 Access to biologic therapies in Canada for children with juvenile idiopathic arthritis. J.Rheum, September 2012
4 Stakeholder Insight: Rheumatoid Arthritis DMHC2592/ Published 09/2010
5 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Patient access threatened by growing demand
and high cost of biologics
Estimated daily treatment costs1
in USD per day
22
The “Biologics Boondoggle”
“A breast cancer patient’s annual cost
for Herceptin is $37,000…
People with rheumatoid arthritis or
Crohn’s disease spend $50,000 a year
on Humira…
…and those who take Cerezyme to treat
Gaucher disease….spend a staggering
$200,000 a year…
“…the top six biologics already consume
43% of the drug budget for Medicare
Part B”
1
Small molecule
drugs
1
Biopharmaceuticals
Source: NY Times, March 2010
Note: All trademarks, logos and pictures are the property of the respective owner
6 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
First wave of high-quality biosimilars are gaining
acceptance
Biosimilars approved in EU
Somatropin
1
5 Epoetin
Filgrastim
6
Source: EMA, Nov 2012
Biosimilar % penetration rates in Daily GCSF class market (Standard Units May 2012)1
Australia
Netherlands
France
Italy
Germany
Spain
Average
UK
Hungary
Finland
Poland
Greece
Bulgaria
Sweden
Czech Republic
Romania
14
Source: IMS Health Standard
Units May 2012
24
25
34
36
38
56
60
61
62
63
67
75
86
Sandoz biosimilars are marketed in over 50 countries and have over 50 million patient
exposure days for the three marketed Sandoz products2
1 Sandoz
analysis / 2Sandoz Risk Management Plan reports 2012
7 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
96
99
Introduction of filgrastim biosimilars in Sep 2008
has significantly increased uptake of G-CSF
G-CSF volume, MAT thru Sep
Number of syringes
7,298
7,579
6,730
5,816
6,160
Filgrastim
Lenograstim
Pegfilgrastim
6,318
3,773
4,394
4,855
3,302
3,329
2.397
2.461
2.370
2.263
2.083
370
461
529
587
641
641
2007
2008
2009
2010
2011
2012
3,259
2.186
This increase in filgrastim use could have potentially increased access
for thousands of cancer patients across Europe
1 Compares Oct 11 - Sept 12 vs Oct 10 - Sept 11 from monthly database
Note: All values MAT thru September of respective year / Source: IMS Quarterly Database
8 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Genuine competition – with a “level playing field” for all
biologics – will lead to increased innovation
Competition and innovation are inextricably linked – a “virtuous circle”
• Originators should be able to
realize fair profit and return on
investment
• Indefinite monopolies lead to
stagnation
• Biosimilars will increase
competition and encourage “next
wave” of biologics innovation
Innovation
Biosimilars
Competition
• Biosimilar development is highly regulated demanding cutting edge
technologies and innovative clinical trial designs, opening the door for
new approaches that can be applied to new technology platforms
9 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Agenda
1
Biologics & biosimilars: An overview
2
Technical development of biosimilars
3
Preclinical development of biosimilars
3
The case for a new paradigm in clinical development
10 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
1. Target definition
Clinical
Reference
product variability
Target range
2. Target directed
development
3. Confirmation
of biosimilarity
PK/PD
Drug product
development
Preclinical
Purification process
development
The target-directed biosimilar dev. concept
1) Develop highly similar product
Initial similarity (tPoS1)
 Pilot scale DS
 Goal posts
Analytical
tool box
Drug substance
Pilot scale
Leveraging biological variability
Drug substance
Final scale
DS / DP3
validation
GLP Tox.
Proof of Similarity
Good Laboratory Practice toxicology studies in animals
3 Drug substance / drug product
11 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
2
Analytics
Analysis reference
Formulation/Drug product
1 Technical
Physicochemical
characterization
Recombinant cell line development
Final biosimilarity
 Validated DS
 Validated DP
Confirm similarity
 Final scale DS
 Final formulation
 In vitro/vivo data
In vitro/vivo
models
Biological
characterization
2) Confirm biosimilarity
Analysis reference
Cell Line
Process
development
Bioprocess development
Phase I
Phase III
(PK/PD)
(confirmatory)
mAbs are complex... but can be thoroughly
characterized using state-of-the-art analytical science
12 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
1
State-of-the-art technologies used to create biosimilars that
match originator products across multiple quality attributes
For monoclonal antibodies typically > 40 different methodologies are applied,
analyzing more than 100 different quality attributes
Primary structure e.g.:
Higher order structure e.g.:
•LC-MS intact mass
•LC-MS subunits
•Peptide mapping
•NMR
•CD spectroscopy
•FT-IR
Impurities e.g.:
•
CEX, cIEF acidic/basic variants
•
LC glycation
•
Peptide mapping deamidation,
•
oxidation, mutation, glycation
•
SEC/FFF/AUC aggregation
Post translat. modif. e.g.:
•NP-HPLC-(MS) N-glycans
•AEX N-glycans
•MALDI-TOF N-glycans
•HPAEC-PAD N-glycans
•MALDI-TOF O-glycans
•HPAEC-PAD sialic acids
•RP-HPLC sialic acids
Biological activity e.g.:
•Binding assay
•ADCC assay
•CDC assay
Combination of attributes e.g.:
•
MVDA, mathematical algorithms
13 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
1
Variability is significant in originator biologics
140
2,0
ADCC Potency
[% of reference]
Unfucosylated G0
[% of glycans]
PostShift
1,6
120
PostShift
100
1,2
0,8
80
0,4
Pre-Shift
60
08.2007
12.2008
Pre-Shift
05.2010
09.2011
Expiry Date
0,0
08.2007
12.2008
05.2010
09.2011
Expiry Date
 Monitoring batches of an approved mAb revealed a
shift in quality
 Shift in glycosylation (structure) pattern results in
different potency in cell-based assays (function)
 Indication of a change in the manufacturing
process
Schiestl, M. et al., Nature
Biotechnology 29, 310-312, 2011)
 Sandoz observed such shifts in several original
products
Note: All trademarks, logos and pictures are the property of the respective owner
14 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Originator variability is the basis for definition of
biosimilarity goal posts
Structure (glyco structure) / function
(ADCC) relationship
700
ADCC (%of Reference)
1
600
500
400
300
200
100
0
0
2
4
6
8
bG0-F [rel. %]
Variability of
reference product
Variability observed during
cell line development
Very narrow goalposts
for biosimilar
15 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Biologically possible variability
QbD elements in biosimilar development
QTPP
 Establish Quality Target Product Profile – the QTPP forms the
basis of design for development of the product
CQAs
 Determine Critical Quality Attributes – linking quality attributes to
clinical safety and efficacy
Process Risk
Assessment
 Linking process parameters and critical material attributes to
CQAs – Definition of critical process parameters (CPPs)
Design Space
 Optional: Define the design space – (multivariate) acceptable
process parameter ranges
Process Knowledge
Control Strategy
 Design and implement control strategy using risk management
e.g. by linking CQAs to process capability and detectability
Continual
Improvement
 Manage product life cycle, including continuous process
verification and continual improvement
16 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Criticality Scoring of Quality Attributes
 Risk assessment for ranking and prioritizing quality attributes
 General concept described in A-MAb case study (Tool #1)
Criticality Score = f(Impact,Uncertainty)
e.g.: Criticality Score = Impact x Uncertainty (A-MAb)
Range
Impact
Uncertainty
Criticality Score
Known or potential consequences
on safety and efficacy, considering:
•Biological activity
•PK/PD
•Immunogenicity
•Safety (Toxicity)
Relevance of information
e.g.
literature
prior knowledge
in vitro
preclinical
clinical
or combination of information
Quantitative measure for an
attribute„s impact on safety and
efficacy.
2 (very low) – 20 (very high)
1 (very low) – 7 (very high)
Using best possible surrogates
for clinical safety and efficacy
2 - 140
Manufacturer„s accumulated experience, relevant information, data
e.g. literature, prior & platform knowledge, preclinical and clinical batches,in vitro studies, structure-function
relationships
17 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Target-directed biosimilar development
Solving the uncertainty dilemma by using full-factorial function
 Scoring of Impact & Uncertainty
Contour Plot of Criticality Score
conceptually similar to A-Mab
(CS range equal 2-140)
uncertainty
 Scores reflect the situation where:
•
QAs we know they have high impact rank
highest
QAs we think they have high impact rank
high
•
QAs we know or think they have a modest
impact rank in the middle
•
QAs we know or think they have no/low
impact rank lowest
50
70
80
90
31
39
73
90
107
24
34
74
95
115
16
28
76
100
123
9
23
77
104
132
2
17
79
109
140
16
20
7
 Calculation using a formula
•
45
cs
<
30 –
55 –
85 –
>
30
55
85
120
120
5
4
3
2
1
2
4
12
impact
 Development guided by CS scoring and at end of development
CS mainly driven by Impact
18 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Quality attributes can be influenced at all stages of
cell line and process development...
1
Cell line
■ Host cell line.
■ Transfection/amplification pool
■ Genetic “set up” of production cell line (clone).
2
Process
■ Growth medium composition.
Quality
■ Culture conditions (pH, T, aeration,...)
■ USP type (batch, fed batch, perfusion,...)
■ USP regime (duration, fed type, perfusion rate..)
■ Culture history (genetic stability, process stability..)
■ Individual DSP steps
■ Hold times
■ Storage (buffer, container, conditions..)
19 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
...but it is the biology that largely determines similarity
Variability of product quality attributes at project start
Screening of host cell lines
Genetics
Screening of transfection pools
Screening of clones
Physiology
Media
development
Bioprocess
development
Chemistry
DSP
dev.
Target spec
20 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
1) Develop highly similar product
Confirming biosimilarity at the structural
and functional level
Initial similarity (tPoS1)
 Pilot scale DS
 Goal posts
Analytical
tool box
2) Confirm biosimilarity
Analysis reference
Drug substance
Pilot scale
Cell Line
Final biosimilarity
 Validated DS
 Validated DP
Confirm similarity
 Final scale DS
 Final formulation
 In vitro/vivo data
Analysis reference
Drug substance
Final scale
DS / DP3
validation
Formulation/Drug product
In vitro/vivo
models
2
GLP Tox.
1 Technical Proof of Similarity
Good Laboratory Practice toxicology studies in animals
3 Drug substance / drug product
Phase I
Phase III
(PK/PD)
(confirmatory)
Biosimilarity exercise = comparison of quality attributes (QAs) of the
biosimilar product with reference product range

Use of a wide range of sensitive and orthogonal analytical methods

Head-to-Head analysis with selected reference batches

Performed on DS and DP level

Comparison of physicochemical and biological characterization
results with head-to-head reference batches and target specification

Comparison of stability data:



intended conditions (=> stability profile)
accelerated, stress conditions (=> forced degradation profile)
Justification of differences in QAs
21 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Target-directed cell line development
Pools
Clones
Clones
Clones
Selected
Cell lines
Clone
Thousands
Hundreds
96/24/6
well plates
Hundreds
Shake flask
22 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Tens
One
Lab- scale bioreactor
Process Dev
.
Pools
Cell line development: Multifactorial selection of best
clone aided by software tool focuses on quality
% glycan structure
100
10
66
AP
SEC
BP
SEC
AP
68
CEX
50
80
60
%
40
0
60
49
50
1.1 1.5 1.4
QUALITY, growth,
productivity, stability
Scoring rules
CQA risk assessment
MAb Knowledge base
20
CEX_2K
CEX_1K
CEX_unk
CEX_0K
CEX_AP
GMAP_unk
GMAP_Man678
GMAP_bG2
GMAP_Man5
GMAP_bG1
GMAP_bG0
0
CEX_2K
CEX_1K
CEX_0K
CEX_unk
Productivity
Unique
Optimization
2.40
-0.01
0.17
0.02
0.49
0.56
0.53
K1-PD
2.27
0.26
0.78
-0.14
-0.09
0.44
0.06
P28
K1-PD
1.47
-0.04
0.79
0.02
-0.12
0.12
0.05
4
5
P30
P56
K1-PD
HPT2
MTX
1.43
1.29
0.00
0.38
0.78
-0.23
0.01
0.59
-0.10
-0.06
0.01
0.03
0.05
0.50
6
7
P56
P57
HPT2
HPT2
MTX
1.11
1.10
0.18
0.27
-0.07
-0.16
0.02
0.56
-0.10
0.17
0.90
-0.40
8
9
P26
P6
K1-PD
K1
MTX
1.04
0.91
-0.09
0.32
0.78
-0.24
0.02
0.14
-0.05
0.11
-0.34
-0.02
0.07
0.40
10
11
12
P34
P33
P43
K1
K1
SSF3
MTX
MTX
0.78
0.69
0.68
0.08
0.14
0.47
0.74
-0.02
-0.03
-0.12
1.00
-0.35
-0.13
0.20
-0.13
-0.22
-0.97
0.10
-0.27
0.01
0.31
13
14
15
16
17
18
19
20
P55
P17
P20
P28
P43
P27
P29
P61
HPT2
SSF3
SSF3
K1-PD
SSF3
K1-PD
K1-PD
HPT2
MTX
MTX
MTX
MTX
MTX
MTX
MTX
0.65
0.57
0.52
0.49
0.47
0.40
0.32
0.31
0.13
-0.17
0.33
0.10
0.15
0.04
-0.10
0.48
-0.06
0.02
-0.36
-0.10
0.17
-0.09
-0.02
-0.82
-0.11
-0.17
-0.42
0.26
-0.25
0.32
0.01
-0.12
-0.06
0.56
0.16
0.27
-0.08
0.27
0.26
0.18
0.62
0.06
0.58
-0.29
0.39
-0.34
-0.02
0.36
0.14
-0.14
0.08
-0.02
-0.15
0.00
0.07
0.36
300
Safety
SSF3
P29
3
Cell_line
MTX
Ranking list
C
A
Similarity
P17
2
200
58
2K
Viability, Titer
64
CEX
Total Score
1
100
65
3.0
bG1 (1-6)
4.0
1K
K1-PD
HPT2
SSF3
K1
HPT1
DG44
0
64
2.0
CEX
3
66
Man8
2
60
5.4
bG1(-F)
All unk peaks
GMAP
1
68
Man7
4
5
Pool
Distance to control (originator)
44
GMAP
K60/p17
K79/p26
GMAP_bG2S1
62
CDC
36
1.7
K56/p17
K93/p17
pool26/G418 K1-PD
pool29/G418 K1-PD
pool30/G418 K1-PD
pool28/G418 K1-PD
pool53/G418 HPT2
Control
GMAP_bG0_F
63
Safety:
K55/p17
K92/p17
CEX_AP
52
K48/p17
K80/p17
GMAP_unk
63
4.0 4.6 4.4
K42/p17
K77/p17
GMAP_Man678
63
4.2
bG1(-N)
Man 8
K40/p17
K62/p17
GMAP_bG2
Man6
Weight = 0
GMAP_Man5
Man5
GMAP
Further
optimization
potential
GMAP_bG2S1
Man3
GMAP
5
GMAP
0
bG2S1
-5
bG2
GMAP
( X-X.DS ) / delta.DS
GMAP
GMAP_bG1
43
K33/p17
K53/p17
bG1_1_3
ADCC
GMAP_bG0
41
3.9 2.2
K32/p17
K36/p17
GMAP
37
K30/p17
bG1_1_6
Safety:
Profile in absolute scale
pool26/G418 K1-PD
pool29/G418 K1-PD
pool30/G418 K1-PD
pool28/G418 K1-PD
pool53/G418 HPT2
40
Man5
bG2S1
K29/p17
0.9 1.2 1.3
K28/p17
bG1_N
GMAP
-10
39
3.8
K24/p17
GMAP
Distance from design specification
GMAP_bG0_F
39
3.8
K23/p17
Gmap GP2017 clones (SF500 FB screening)
38
4.2
1.4
bG0
unk8
K11/p17
K15/p17
80%
40
3.0
K7/p17
bG0_N
38
K3/p17
bG0_N_F
39
K126/p26
36
bG0 (-F)
Man 7
K122/p26
bG1_F
39
K106/p26
46
K84/p26
49
K59/p26
GMAP
GMAP
half life
36
bG0 (-N)
bG2
K53/p26
bG0_F
GMAP
45
K52/p26
K104/p26
46
K22/p26
44
K7/p26
47
Man 4
unk6
K1/p26
GMAP
44
K75/p17
62
K72/p17
Efficacy:
35
K69/p17
100%
47
K64/p17
D
35
bG0 (-N-F)
unk5
K59/p17
60%
46
2.3
K38/p17
2.0 2.3 1.9 1.6 1.8 1.8 3.3 2.2 2.4 2.9
0.9 1.7
2.2 2.5 3.1 2.5 2.8 2.8 3.1 0.9
Unk group
Man 6
K37/p17
63
1.1 1.5
K25/p17
K31/p17
B
66
5.2
K13/p17
K22/p17
71
0.6 3.1
Man3*
bG1 (1-3)
K9/p17
K17/p17
40%
20%
0%
GP2017
Software toolbox ARA
23 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Efficacy
0.14
0.39
Cell line development: Multiple selection rounds
required to hit the target
Individual Value Plot of Total Score
5.0
2.5
P7
Total Score
0.0
P13
-2.5
-5.0
Selected clone and backup
clone for further process
development
-7.5
-10.0
-12.5
1. Pools 50 mL
2. Clones 120 mL
3. Clones 1 L
24 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
4. Clones 5 L
5. Originator
Structural characterization
The rituximab biosimilar example : Primary sequence
 Intact mAb Mass and HC & LC by RP-HPLC-ESI-MS - comparable
 Amino acid sequence by RP-HPLC-ESI-MS/MS - identical
 RP-HPLC-UV/MS - comparable
 Free thiols by Ellman‟s assay - comparable
Visser, J. et al., BioDrugs, May 2013
Reference product
Reference product
DS
DP
mass
1,9
5,0
10,0
15,0
20,0
25,0
30,0
35,0
40,0
43,6
min
MS spectra of biosimilar mAb and originator mAb HC & LC
Peptide map of biosimilar mAb and originator - mAb
25 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Structural characterization
The rituximab biosimilar example : Higher Order Structure – CD, FTIR, HDX
 Circular Dichroism Spec. (near & far UV) - comparable
CD [mdeg/((g/l)cm))]
2
1
750
0
-1
260
270
280
290
300
310
320
330
340
550
350
-2
-3
Reference product
DP
150
-4
Reference product
DP
-5
-6
-50
200
210
220
230
240
250
260
-250
-7
-450
-8
Wavelength [nm]
Wavelength [nm]
 FTIR Spec. – comparable
1500
1400
1300
Reference product
DP
1700
1600
1500
1400
1300
Wavenumber cm-1
1200
0.00 0.05 0.10 0.15 0.20 0.25
Absorbance Units
1600
0.00 0.05 0.10 0.15 0.20 0.25
1700
 H/D Exchange – comparable
1200
26 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Structural characterization
The rituximab biosimilar example : SPR binding assays
 Surface Plasmon Resonance Fc-receptor binding assays
Reference KD
GP2013 KD
FcRn
0.55-0.58 µM
0.54-0.58 µM
FcϒRIa
10.4-11.8 nM
10.9-12.4nM
FcϒRIIa
2.4-2.7 µM
2.4-2.7 µM
FcϒRIIb
11.4-12.8 µM
11.0-12.7 µM
FcϒRIIIa F158
7.4-10.3 µM
8.5-10.9 µM
FcϒRIIIa V158
3.5-4.9 µM
4.2-5.0 µM
FcϒRIIIb
9.2-11.7 µM
9.9-12.4 µM
Rituximab biosimilar (GP2013) is functionally indistinguishable
from its reference product
27 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Agenda
1
Biologics & biosimilars: An overview
2
Technical development of biosimilars
3
Preclinical development of biosimilars
3
The case for a new paradigm in clinical development
28 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Function: mAbs possess multiple functions
ADCC
Antibody dependent
cellular cytotoxicity
CDC
complement
dependent
cytotoxicity
Effector cells
(NK cells)
C1
Target cell
Fc g RIIIa
Target cell
Membrane
attack
complex
PCD
Programmed cell death ( apoptosis )
Blocking / Inhibiting RB
Soluble Target
29 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
29
Cell line development case study: Minor glycan structures and
ADCC bioactivity – attention to detail is essential...
Characterization of mAB glycosylation heterogeneity
High resolution identification and
quantification of major (G0,G1,G2)
and minor glycan structures
(down to a level of 0.1 rel.%)
2x
Targeting ADCC activity and fucosylation by clone selection
10
ADCC (%of Reference)
700
600
8
bG0(-F) [%]
500
400
300
200
6
4
2
100
0
0
0
2
4
6
8
Originators
bG0-F [rel. %]
30 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Parental
Cells
Pool 18
Pool 16
Clone 19
30
In-vitro comparability: ADCC assays using clinical
scale GP2013 (rituximab) drug product
Daudi cell line & fresh effector cells
SU-DHL4 & fresh effector cells
Further cell lines tested:
•Raji
•Z138

ADCC comparable to MabThera® / Rituxan®
31 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Structural characterization
The rituximab biosimilar example : Bioassays
 Potency bioassays designed to give quantitative results
Target binding
ADCC
CDC
Apoptosis
(n = 30 / 9)
(n = 50 / 9)
(n = 50 / 9)
(n = 7 / 5)
GP2013
101 - 108 %
96 - 105 %
102 - 111 %
88 - 97 %
Reference range
96 – 107 %
70 – 132 %
95 – 127 %
88 – 102 %
CDC
complement
dependent
cytotoxicity
Effector cell
(NK cells)
C1
FcgRIIIa
Target cell
Membrane
attack
complex
Blocking
PCD
32 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
ADCC
Antibody dependent
cellular cytotoxicity
In-vivo comparability:
Two models for non-Hodgkin’s lymphoma
SU-DHL-4 model
Jeko-1 model

Efficacy is similar
33 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
In-vivo comparability:
PK following i.v. administration to primates
Design
Study groups: MabThera® and GP2013 , n=14 cyno. monkeys / group
Dose Level:
5 mg / kg single i.v. infusion

PK: AUC and Cmax are similar
34 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
In-vivo comparability:
B-cell depletion following i.v. infusion
Design
Study groups: MabThera® and GP2013 , n= 14 cyno. monkeys / group
Dose Level:
5 mg/kg i.v. administration
0,80
B-cell Count [109/L]
0,70
0,60
0,50
GP13 CD20low
0,40
MabThera® CD20low
0,30
GP13 CD20high
MabThera® CD20high
0,20
0,10
0,00
0
1
2
3
4
5
6
7
8
9
10
11
12
Time [weeks]

PD: B-cell depletion is similar
35 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
13
14
Agenda
1
Biologics & biosimilars: An overview
2
Technical development of biosimilars
3
Preclinical development of biosimilars
3
The case for a new paradigm in clinical development
36 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
2
Goal of pre-clinical and clinical development is to confirm
biosimilarity and not to prove de novo efficacy
6 – 12 m
1
9 – 12 m
2
Pre-clinic
Abbreviated
toxicology, efficacy/
safety in relevant
species models
2 – 4 yrs
3
PK/PD Ph I/II
Demonstrate PK/PD
equivalence in a
sensitive population
- can be healthy
volunteers
Time
4
Efficacy/Safety
Ph III
Design tailored to
demonstrate
biosimilarity, but not
patient benefit per se
•Sensitive indication
•Trial design might be
different, e.g., endpoints
Key challenges are
patient recruitment
and reference supply
37 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Post-approval
Additional data to
meet regulatory
needs
2
Clinical development requires strong scientific and
operational capabilities
Key success factors
Desired outcome
Example: Epoetin alfa
• Know how to design innovative
studies and negotiate with health
authorities
• Patient recruitment supported by
strong clinical networks & company
credibility
Biosimilar (n = 60)
Originator (n = 34)
• Strong clinical operations skills
Weigang-Köhler et al. Onkologie 2009; 32: 168-74
38 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Clinical trials are not sensitive enough to
differentiate different anti-TNF biologics
100
 Response Rates of anti-TNFs vary depending on study protocols
ACR20 Response Rate [%]
90
80
70
71
66
67
59
60
50
50
40
30
60
55
33
33
27
28
20
20
14
14
10
0
ETA
Weinblatt
Weinblatt 1999
ADA DE019
Weinblatt 2004
Week 12 (14*)
IFX ATTRACT #
Maini 1999
Week 12 PLO
CTZ RAPID 1
GLM
GLMGO-FORWARD
GO- *
*
Keystone
2011
FORWARD
Keystone 2008
Week 24 (30#)
39 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Week 24 PLO
SPR real time binding assay is much more sensitive to
differentiating anti-TNFs and demonstrating biosimilarity
Kaymakcalen, et al: Clinical Immunology, (2009) 131, 308-316
www.diahome.org
40 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
Focus of the clinical development program
 Confirm the similarity shown during the physicochemical, biological and nonclinical characterization in a clinical setting
• No need to show efficacy/safety de novo (has been established for the
reference product)
• Requires “sensitive setting“ to detect potential differences
 Select a sensitive model for the clinical trial
• Use of novel endpoints, biomarkers, and populations
• PK/PD studies in healthy volunteers may be more sensitive than trials in a
disease area
– less confounding factors
– Healthy volunteers more responsive
• If a comparative (Phase 3) trial is needed, select an indication
– with a large effect size for the selected endpoint
– where immunogenicity can be reliably assessed
– which allows extrapolation to other indications
41 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013
SUMMARY
 Today, biologics can be thoroughly characterized and understood both
structurally and functionally
 The goal posts for biosimilar development are set by the variability in the
reference product and a thorough understanding of the molecule
 Using extensive cell line, process development, and analytical capabilities,
biosimilars (including mAbs) can be engineered to be highly similar
 Target-directed development provides safe and effective products
 A high level of structural and functional similarity lays the foundation for
biosimilarity and should allow for a selective and targeted (pre)clinical approach
and extrapolation of indications
 Biosimilar development relies upon and provides the thorough understanding of
the pharmacological properties of biologics, and consequently their utility and
applicability to development across scientific innovation. As such, they provide a
platform for innovation at the scientific, technical, clinical, regulatory and
pharmaceutical advance in health care.
42 | Technological Innovation in Healthcare – Biologics – A. da Silva | Brasilia, 26 June 2013