process optimization qbd 20180507 - unimi.it

1Textmasterformat in Mastervorlage eingeben1

QbD conference – 08.05.2018 Milan


Process optimisation considering QbD and using DoE –the basis for scale up

Stefanie Keser / Glatt Pharmaceutical Services GmbH & CO. KG


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


“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



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 ???



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



1.3. Process Validation

Traditional process validation: „turn a blind eye“ approach

• 3 process validation lots

� after process validation: „principle hope“



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



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).



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)


Phase 3: Continued Process Verification


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



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)



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



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



1. QbD Approach

Design space

• Develop a design space

Control strategy

• Design and implement a control stategy

Continiousimprovement

• Manage product life cycle, includungcontinual improvement



2. Case Study 1Optimisation of modified release coating



sugar beads

drug layer

seal coating

MR coating

final coating

2. Modified release coating (MR) process

Dissolution profile

0

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



Modified release coating

• Eudragit RS / RL

• triethylcitrate

• talc

• water

• 20% solids concentration

2. Modified Release / pulsatile pellet

formulation



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



2. Important configuration and processing

parameters


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


• 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



• 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



2. Coating parameters and ranges


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


2. STAVEX Vertex-Centroid Design / 19 trials


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


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



2. Impact of fluid bed impact factors on the

in-vitro dissolution profiles

0

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



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


Result:

< 10% dissolution at highest inlet

air volume




Constant:

• inlet air volume = 130 m³/h

• atomisation air pressure = 2,6 bar

Variable:• product temperature


Result:

< 10% dissolution at lowest inlet air

and product temperature





Constant:

• inlet air volume = 150 m³/h

• atomisation air pressure = 2,6 bar

Variable:• product temperature


Result:Product temperature <30°C result in

dissolution <10%



Constant:• product temperature = 33°C

• inlet air temperature = 65°C

Variable:• atomisation air pressure


Result• only inlet air volume has an

impact on the in vitro

dissolution profile

• no impact of atomisation air

pressure



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



• 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

0

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



2. Knowledge and Design space

Knowledge space Design space


Limits exceeded


3. Case Study 2Optimization of drug layering process



• 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)



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


H

H

Overall targets

- High product quality

- High process efficiency

- Robustness

- Reproducibility

- Relationships process parameter to

product quality


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



3. Two level full factorical design

at lab scale (6“ Wurster)


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


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)



DL 3 and DL 4 with highest fraction of DL pellets > 200 µm (agglomerates):

3. Particle size distribution of DL pellets



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)



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)



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



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



3. DL process after optimisation

• process time reduced by ~ 50%

• low level of agglomerates > 250 µm


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


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



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



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!



Back up slides



Case Study 3Optimisation of modified release coating process



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 (

%)



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



Goal: in vitro dissolution kinetics following 1. order

• study design similar to case study 2 with Eudragit RL / RS processing

parameters to be tested:



• product temperature

• atomisation air pressure

4. Impact of fluid bed impact factors on the

in-vitro dissolution profiles of

modified release pellets




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)



• 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


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



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



Related documents

ICH Q8 (R1) Pharmaceutical Development

ICH Q9 Quality Risk management

ICH Q10 Pharmaceutical Quality System


process optimization qbd 20180507 - unimi.it

Documents