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QbD conference – 08.05.2018 Milan
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Process optimisation considering QbD and using DoE –the basis for scale up
Stefanie Keser / Glatt Pharmaceutical Services GmbH & CO. KG
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1. Quality by design
2. Case study 1:
Optimisation of modified release coating process
3. Case study 2:
Optimisation of drug layering process
4. Summary
Agenda
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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“Systematic process to build quality into a product “
• Quality can not be tested into product
1. Quality by design
http://www.biotrains.com/course/quality-by-design/, access: 20.04.2018
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.1. Current quality assurence
Doug Dean and Frances Bruttin, PwC Consulting, FDA Science Board Meeting, NOV16,2001
Sigma ppm defective yield costs of QA
2 σ 308 537 69,20% 25 - 35 %
3 σ 66 807 93,30% 20 - 25 %
4 σ 6 210 99,40% 12 - 18 %
5 σ 233 99,98% 4 - 8 %
6 σ 3,4 100,00% 1 - 3 %
7 σ 1
Pharma
Microelectronics
� What is wrong with the pharmaceutical industry ???
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.2. Quality by endproduct testing
Doug Dean and Frances Bruttin, PwC Consulting, FDA Science Board Meeting, NOV16,2001
Sigma ppm defective yield costs of QA
2 σ 308 537 69,20% 25 - 35 %
3 σ 66 807 93,30% 20 - 25 %
4 σ 6 210 99,40% 12 - 18 %
5 σ 233 99,98% 4 - 8 %
6 σ 3,4 100,00% 1 - 3 %
7 σ 1
Pharma
Delivered to
patient
�FDA : Initiative to enhance and modernize the regulation of
pharmaceutical manufacturing and product quality (2002)
Quality tested
into product
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.3. Process Validation
Traditional process validation: „turn a blind eye“ approach
• 3 process validation lots
� after process validation: „principle hope“
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.3. Process Validation:
General Principles and Practices (2011)
• Links product and process developmentto commercial manufacturing process
� lifecycle concept
� process improvement and innovationthrough sound science
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.3. Process Validation:
General Principles and Practices (2011)
Phase 1: Process Design
Commercial process is defined based on knowledge gained through
development and scale up activities.
Phase 2: Process Qualification
Process design is confirmed as being capable of reproducible commercial
manufacturing (Confirmation Batches).
Phase 3: Continued Process Verification
Ongoing assurance is gained during routine production that the process
remains in a state of control (Product Quality Review PQR).
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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phase I phase II phase III registration market launch
preclinical development clinical development
analytical characterisation / compatibility studies / stability studies
formulation development
process development
CTM supply for phase I and II
formulation & process optimisation
scale-up to pilot scale
CTM supply phase III
registration batches
scale-up to commercial & validation
launch / commercial Supplies
developmentof API
preclinicalStudies
Process Validation
Phase 1:
Process Design
Phase 2: Process Qualification
1.3. Process Validation (2011)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
Phase 3: Continued Process Verification
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1.4. QbD Approach
QTPP
• Define the Quality Target Product Profile
• Meet the requirements of the patients
CQAs• Determine the Critical Quality Attributes
Riskassessment
• Links CQAs to Critical Material/ ProcessParameters
• Evaluates potential risks
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.4. QbD Approach: Risk assessment
• Cause and Effect diagrams (Fish bone / Ishikawa diagram)
• FMEA (Failure Mode and Effect Analysis)
• importance
• occurence probability
• discovery probability
�risk priority number (RPN) for each Critical Process Parameters
�rating of risk priority numbers
�definition parameters to be investigated in DoE
Chao et al.
fishbone diagram (Chao et al)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.4. QbD Approach
TPP
• Define the Target Product Profile
• Meet the requirements of the patients
CQAs• Determine the Critical Quality Attributes
Riskassessment
• Links CQAs to Critical product/process parameters
• Evaluates potential risks
• Determines parameters to be investigated
CPPs
• Evaluated via Design of Experiments
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1.4. QbD Approach: Design of experiments
1. Choose experimental design
�Conduct preliminary experiment � define factor range
2. Conduct randomized experiments
3. Analyse data
4. Create multidimensional surface model
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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1. QbD Approach
Design space
• Develop a design space
Control strategy
• Design and implement a control stategy
Continiousimprovement
• Manage product life cycle, includungcontinual improvement
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. Case Study 1Optimisation of modified release coating
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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sugar beads
drug layer
seal coating
MR coating
final coating
2. Modified release coating (MR) process
Dissolution profile
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20
40
60
80
100
120
0 120 240 360 480 600 720 840 960 1080 1200
tim e (m in)
dis
so
luti
on
ra
te (
%)
• 8 hrs lagtime
• pulsatile release profile
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Modified release coating
• Eudragit RS / RL
• triethylcitrate
• talc
• water
• 20% solids concentration
2. Modified Release / pulsatile pellet
formulation
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. CQAs
Description of CQA Target dimension
1 particle size 1 - 1,25 µm 100 %
2 practical yield of product 100 %
3 coating weight gain 100 %
4 assay 100 %
5 in-vitro dissolution / 6 hours < 3 %
6 in-vitro dissolution / 8 hours < 10 %
7 in-vitro dissolution / 10 hours 50 %
8 in-vitro dissolution / 12 hours 100 %
9 in vitro dissolution t50% 10 hours
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. Important configuration and processing
parameters
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
WURSTER partition height
inlet air distribution plate
spray nozzle configuration
batch size
inlet air volume inlet air temperature
atomisation air pressure
product temperature
spray rate
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• WURSTER partition height
• inlet air distribution plate
• spray nozzle configuration
• batch size
• inlet air volume
• inlet air temperature
• atomisation air pressure
• spray rate / product temperature
2. Potential critical process parameters
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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• WURSTER partition height
• inlet air distribution plate
• spray nozzle configuration
• batch size
• inlet air volume � fluidisation
• inlet air temperature � sticking of polymer
• atomisation air pressure � droplet size
• spray rate / product temperature � humidity / sticking of polymer
2. Critical process parameters
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. Coating parameters and ranges
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
levels
low medium high
inlet air volume 110 130 150
atomisation air
pressure1,4 2 2,6
product temperature 27 30 33
inlet air temperature 45 55 65
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2. STAVEX Vertex-Centroid Design / 19 trials
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
No. inlet air volumeatomisation air
pressureproduct
temperatureinlet air
temperature
(m³/h) (bar) (°C) (°C)
1 110 1,4 27 65
2 110 2,6 27 45
3 110 2,6 27 65
4 110 2,6 33 65
5 150 1,4 33 45
6 150 2,6 27 65
7 130 2 30 55
8 110 1,4 30 45
9 130 1,4 27 45
10 110 1,4 33 55
11 110 2 33 45
12 130 1,4 33 65
13 130 2,6 33 45
14 150 1,4 27 55
15 150 2 27 45
16 150 1,4 30 65
17 150 2 33 65
18 150 2,6 30 45
19 150 2,6 33 55
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findings
• 2 batches stopped� lump formation � high inlet temperature (65°C)
• Low inlet temperature & low spray pressure � no sticking
• Stickyness temperatur dependend
• Most problematic : high inlet temperatrue, high product temperature, low inlet air flow rate
• Spray pressure uncritical
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. Impact of fluid bed impact factors on the
in-vitro dissolution profiles
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20
40
60
80
100
120
0 120 240 360 480 600 720 840 960 1080 1200
dis
so
luti
on
ra
te (
%)
time (min)
1 / R0520-01-09A 3 / R0520-01-11A 4 / R0520-01-12A 10 / R0520-01-16A
11 / R0520-01-18A 8 / R0520-01-15A 7 / R0520-01-14A 9 / R0520-01-17A
14 / R0520-01-23A 15 / R0520-01-26A
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. In-vitro dissolution 8 h (goal: < 10%)
Constant:• atomisation air pressure = 2,6 bar
• inlet air temperature = 65°C
Variable impact factors:• product temperature
• inlet air volume
Result:
< 10% dissolution at highest inlet
air volume
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. In-vitro dissolution 8 h (goal: < 10%)
Constant:
• inlet air volume = 130 m³/h
• atomisation air pressure = 2,6 bar
Variable:• product temperature
• inlet air temperature
Result:
< 10% dissolution at lowest inlet air
and product temperature
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. In-vitro dissolution 8 h (goal: < 10%)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
Constant:
• inlet air volume = 150 m³/h
• atomisation air pressure = 2,6 bar
Variable:• product temperature
• inlet air temperature
Result:Product temperature <30°C result in
dissolution <10%
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2. In-vitro dissolution 8 h (goal: < 10%)
Constant:• product temperature = 33°C
• inlet air temperature = 65°C
Variable:• atomisation air pressure
• inlet air volume
Result• only inlet air volume has an
impact on the in vitro
dissolution profile
• no impact of atomisation air
pressure
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Process Parameters (Factors)inlet air
volumeatomisation
air pressureproduct
temperatureinlet air
temperature
Response Factors
1 size 1 - 1,25 mm
2 practical yield
3 practical yield CR coating
4 assay
5 dissolution 8 hours
6 dissolution 10 hours
7 dissolution t 50%
probably important
possibly important
arbitrary
2. Impact of fluid bed parameters on the quality of modified release pellets
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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• moderate inlet air temperature (45 – 55°C)
• moderate product temperature (27 – 30°C)
• high fluidisation air volume
• atomisation air pressure: no impact
intact film damaged film
2. Feasible parameters for the coating with
Eudragit RS / RL aqueous dispersion
Dissolution profiles from a Multifactorial Design Study
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20
40
60
80
100
120
0 120 240 360 480 600 720 840 960 1080 1200
time (min)
dis
so
luti
on
rate
(%
)
� avoid intermediate sticking of pellets with moderate temperatures and
vigorous fluidization
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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2. Knowledge and Design space
Knowledge space Design space
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
Limits exceeded
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3. Case Study 2Optimization of drug layering process
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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• Established process
• Fluidized bed system (Wurster – process)
• Drug layering (DL) on starter pellets 150 µm
• water-based process
Goal:
• process optimisation for regular GMP manufacturingand scale up
• robust process and reproducible product quality
3. Manufacturing process to be optimized:
Drug layering (DL) process (case study 1)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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3. Critical Quality attributes
Critical quality attributes
Process:
1 High yield
2 Short process time
Product:
3 Assay (95 – 105 %)
4 Minimal amount of oversized particles
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
H
H
Overall targets
- High product quality
- High process efficiency
- Robustness
- Reproducibility
- Relationships process parameter to
product quality
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Process parameter
Inlet air temperature °C 70 ± 20
Atomisation air pressure bar 2.5 ± 0.5
Inlet air volume m3/h 700 - 850
Mean spray rate g/min 75
Spraying time h 10
Mass of spraying liquid kg 45
Yield practical % 96 – 101
Oversized particles > 250 µm % 1 – 2
Weight gain % 80 – 100
3. DL process in pilot scale (18“ Wurster):
the starting point before optimisation
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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3. Two level full factorical design
at lab scale (6“ Wurster)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
Inlet air temperature [°C]
Sp
ray r
ate
[g
/min
]
Process parameter Low level
High Level
Inlet air
temperature [°C]
65 70
Spray rate [g/min] 10 17
Atomisation air
pressure [bar]
2,5 4
• Drug layering process was scaled down to a 6“ Wurster
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Impact factors Response variables Batch Inlet air
temper.
Max
spray
rate
Atomisa-
tion air
pressure
Yield Weight
gain
Assay Oversized
particles
>250 µm
Amount
fines
<125 µm
LOD end
of
spraying
LOD
after
drying
Mean
spray
rate
Spray
time
Min.
product
temper.
Bulk
density
°C g/min bar % % % % % % % g/min min °C g/ml
DL 1 65 10 2.5 92.8 62.5 98.5 0.6 5.2 2.6 2.2 9.60 124 48.8 0.81
DL 2 70 10 2.5 92.6 61.0 94.5 0.8 4.6 1.8 1.8 9.60 124 51.4 0.83
DL 3 65 17 2.5 95.6 77.1 97.9 1.8 5.6 3.0 2.6 12.27 97 42.9 0.78
DL 4 70 17 2.5 95.4 75.7 99.2 1.4 5.6 2.6 2.5 12.39 96 45.8 0.81
DL 5 65 10 4 95.1 74.3 102.0 0 7.2 2.4 2.4 9.67 123 48.1 0.83
DL 6 70 10 4 97.2 84.1 104.7 0 6.0 1.8 2.0 9.64 111 51.6 0.89
DL 7 65 17 4 94.2 69.4 96.5 0 5.0 3.1 2.7 12.39 96 41.6 0.86
DL 8 70 17 4 93.0 63.3 98.9 0 5.7 2.6 2.3 12.14 98 44.6 0.83
3. Two level full factorial design:
results of lab scale (6“ Wurster)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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DL 3 and DL 4 with highest fraction of DL pellets > 200 µm (agglomerates):
3. Particle size distribution of DL pellets
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Atomisation air pressure [bar]
• no particles > 250 µm with
AAP > 3,5 bar for all spray rates
• process in a robust state
• low risk for particle agglomeration
Particles > 250 µm (%)
0% 0,5 % 1 %
3. Impact of spray rate and atomisation air
pressure (AAP)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Atomisation air pressure [bar]
• assay > 98% with high AAP (small
droplets) and moderate spray
rates
= risk for spray drying
• robust DL formulation and
process with at broad range of
parameters
Assay [%]
98,0 % 100 %
3. Impact of spray rate and atomisation air
pressure (AAP)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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3. Regression analysis
• quantitative ranking list according to R2
• identification of the impact factor with the strongest influence on response
variable
• correlation between response variables (e.g. weight gain and assay)
• evaluation of precision changes on all levels
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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3. Regression analysis for DL process
P a r a m e t e r 1 P a r a m e t e r 2 S lo p e m
q u a l i t a t iv e R 2
Im p a c t f a c t o r s
A t o m is a t io n a ir p r e s s u r e O v e r s i z e d p a r t ic le s > 2 5 0 µ m - 0 .7 4 4
B u lk d e n s i t y + 0 .5 1 9
A s s a y + 0 .2 5 9
W e ig h t g a in 0 0 .0 5 7
A m o u n t f in e p a r t ic le s < 1 2 5 µ m + 0 .2 4 7
M e a n s p r a y r a t e L O D e n d o f s p r a y in g + 0 .6 0 7
L O D e n d o f d r y in g + 0 .5 4 5
A s s a y 0 0 .0 8 9
W e ig h t g a in 0 0 .0 7 7
M in im a l p r o d u c t t e m p e r a t u r e - 0 .8 0 4
O v e r s i z e d p a r t ic le s > 2 5 0 µ m 0 + 0 .1 1 6
R e s p o n s e v a r ia b le s
W e ig h t g a in A s s a y + 0 .5 3 1
A m o u n t f in e p a r t ic le s < 1 2 5 µ m + 0 .2 8 8
A m o u n t f in e p a r t ic le s < 1 2 5 µ m A s s a y + 0 .5 9 6
M in im a l p r o d u c t t e m p e r a t u r e L O D e n d o f s p r a y in g - 0 .9 0 6
O v e r s i z e d p a r t ic le s > 2 5 0 µ m 0 0 .0 4 1
W e ig h t g a in 0 0 .0 0 0
+ = p o s i t iv e s lo p e / d i r e c t r e la t io n s h ip
- = n e g a t iv e s lo p e / in v e r s r e la t io n s h ip
0 = s lo p e n e a r ly z e r o / n o r e la t io n s h ip
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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3. DL process after optimisation
• process time reduced by ~ 50%
• low level of agglomerates > 250 µm
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
Process parameter Before optimisation best in DoE proposal for Test 1 Test 2
18” W.
pilot scale
6” W.
lab scale
18” W.
pilot scale
18” W.
pilot scale
18” W.
pilot scale
Inlet air temperature °C 70 ± 20 70 65 – 70 70 70
Atomisation air pressure bar 2.5 ± 0.5 4.0 3.5 ± 0.5 3.5 4.0
Inlet air volume m3/h 700 - 850 80 700 → 850 700 → 850 700 → 850
Mean spray rate g/min 75 12.3 ∼ 90 120 120
Product temp. during
spraying
°C 50 43 – 55 45 - 53 46 – 49 47 – 50
Yield practical % 96 – 100 93 97 97 97
Spraying time h 10 1,6 6,3 4,8 4,8
Oversized particles >250 µm % 1 - 1.5 0 ∼ 0.5 1.6 3.1
Assay practical (relative) % 100 99 100 102 102
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4. Summay
�Design of Experiments (DoE) useful tool to understand and optimise process
�Design Space (DS) = reproducible product quality in small and large scale
applying a defined range of processing parameters
�this is Quality by Design (QbD)
�providing a state to the art Process Validation
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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600kg 600kg
process
development
7“ Wurster
process optimisation
(DoE)
7“ Wurster
scale up to pilot
18“ Wurster
scale up to
commercial I
32“ Wurster
scale up to
commercial II
46“ Wurster
start up commercial equipment +
process validation
46“ Wurster
number of trials
4kg 4kg 80kg 300kg
32 2
3
600kg
commercial
production
startup incl
process
validation
process development
and scale up
14
26
8
4. Process development / scale-up based on
Design Space
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Stefanie Keser
Project manager
Glatt Pharmaceutical Services
Werner-Glatt-Strasse 1
79589 Binzen / Germany
Phone: +49 – 7621 664 4075
E-mail: [email protected]
Dr. Norbert Pöllinger
Head of Development and Manufacturing Services
Glatt Pharmaceutical Services
Werner-Glatt-Strasse 1
79589 Binzen / Germany
Phone: +49 – 7621 664 707
Fax: +49 – 7621 664 728
E-mail: [email protected]
Thank you for your attention!
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Back up slides
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Case Study 3Optimisation of modified release coating process
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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sugar beads
drug layer
seal coating
MR coating
final coating
• 1. order drug release
• ~ 60% after 3 hrs
• ~ 80% after 6 hrs
4. Optimisation Modified release coating (MR)
process 2
44212 Controlled Release Pellets Prototype 27,5% :
Eudragit NE 30 D coating / 20% coating level
in vitro dissolution target profile
0
20
40
60
80
100
120
0 100 200 300 400 500 600 700 800
batch no.
dru
g r
ele
ase (
%)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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sugar beads
drug layer
seal coating
MR coating
top coating
4. Modified release coating formulation
• Eudragit NE 30 D
• HPMC
• talc
• water
• solids concentration: 20% w/w
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Goal: in vitro dissolution kinetics following 1. order
• study design similar to case study 2 with Eudragit RL / RS processing
parameters to be tested:
• inlet air volume
• inlet air temperature
• product temperature
• atomisation air pressure
4. Impact of fluid bed impact factors on the
in-vitro dissolution profiles of
modified release pellets
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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44212 Controlled Release Pellets Prototype 27,5% :
Eudragit NE 30 D coating / coating level: 20%
STAVEX study results
0
20
40
60
80
100
120
0 100 200 300 400 500 600 700 800
batch no.
dru
g r
ele
ase (
%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
4. In-vitro dissolution profiles
6 hrs: 80% +/- 10% (preliminary specification)
3 hrs: 60% +/- 10% (preliminary specification)
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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• moderate inlet air temperature (40°C)
• low product temperature (25°C)
• high fluidisation air volume
• atomisation air pressure: no impact
4. Feasible parameters for the modified
release coating with Eudragit RS / RL
aqueous dispersion
44212 Controlled Release Pellets Prototype 27,5% :
Eudragit NE 30 D coating / coating level: 20%
STAVEX study results
0
20
40
60
80
100
120
0 100 200 300 400 500 600 700 800
batch no.
dru
g r
ele
ase (
%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Eudragit NE 30 D / HPMC modified release coating:
a more tolerant formulation / process than Eudragit RL / RS 30 D
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Other Guidance documents
• PAT – A frramework for innovative Pharmaceutical Development,
Manfacturing and Quality Assurance 2004
• Formal dispute resolution: Scientfic and technical issues related to
pharmaceutical cGMP 2006
• Quality systems Approach to Pharmaceutical cGMP regulations
2006
• cGMP for phase I investigational drugs 2008
• ICH Q8, Q9, Q10
• Process validation 2011
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser
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Related documents
ICH Q8 (R1) Pharmaceutical Development
ICH Q9 Quality Risk management
ICH Q10 Pharmaceutical Quality System
Process optimisation considering QbD and using DoE – the basis for scale up / Stefanie Keser